Identifying customer patterns for energy
services in a dynamic price setting
Navid Sadat-Razavi
Rotterdam School of Management
Erasmus University Rotterdam
A thesis submitted for the degree of
Master of Science
Business Information Management
12th July 2016
Master Thesis – Navid Sadat-Razavi
Student Information
Name:
Student Number:
Study Programme:
Navid Sadat-Razavi
441482
Msc Business Information Management
Submission Date:
12.07.2016
Graduation Committee
University Coach: Ir. Derck Koolen
Phd Candidate
Department of Technology and Operations Management
Rotterdam School of Management (RSM)
Co-Reader: Dr. Yashar Ghiassi-Farrokhfal
Assistant Professor
Department of Technology and Operations Management
Rotterdam School of Management (RSM)
External Coach: Mark Schütz
Managing Director Utilities and Strategy Transformation
Capgemini Consulting
Acknowledgements
The work in this thesis was supported by Capgemini
Nederland B.V. Their cooperation is gratefully
acknowledged.
Special thanks go to Mark Schütz (Managing
Director Utilities), Jeroen van Daal (Principal) and
Arie Hobbels (Consultant) at Capgemini.
The work in this thesis was supported by Qurrent
Energie. Their cooperation is gratefully acknowledged.
Special thanks go to Mark van Loon, Business
Development Manager at Qurrent Energie.
The work in this thesis was supported by Vereniging
Eigen Huis. Their cooperation is gratefully
acknowledged.
2
Master Thesis – Navid Sadat-Razavi
Preface
The copyright of the master thesis rests with the author. The author is responsible for its contents.
RSM is only responsible for the educational coaching and cannot be held liable for the content.
The author declares that the text and work presented in this Master thesis is original and no sources
other than those mentioned in the text and its references have been used in creating this Master thesis.
3
Master Thesis – Navid Sadat-Razavi
Executive Summary
The main aim of our study was to examine customer patterns of residential households in a dynamic
price setting. With the development of new technologies such as smart meters, utility providers have the
opportunity to inherently change the way they are communicating with their customers. One way the
utility providers are aiming to improve the interaction with their customers is by introducing
dynamically changing electricity prices, based on real electricity market prices. However, less is
currently known about consumer preferences and response to dynamic prices. Given that different
people can have a diverse set of values and preferences, it is assumed that their reaction to changing
electricity prices is quite diverse. Therefore, we have asked ourselves how household characteristics and
dynamic electricity prices influence household electricity consumption in dynamic price settings. We
believe that clarifying the relationship of these two components with electricity consumption behavior
can deliver valuable insights for future energy services.
We have conducted three separated analyses, investigating the influence of household attributes on
households’ willingness to use electricity (RQ1), the influence of dynamic electricity prices on
household behavioral electricity patterns (RQ2), and a household segmentation based on household
attributes that are capable of reflecting behavioral patterns of electricity consumers (RQ3). Our data set
was obtained from a pilot project of a Dutch energy supplier called ‘Qurrent Energie’, in which
households were (and still are) provided with dynamic electricity prices. We are using a set of panel data
regressions, a principal component analysis, and a k-means analysis to derive at our results.
Our findings have shown that the willingness to use electricity of households exposed to dynamic price
settings is lower as compared to household exposed to the usual Time-of-Use price settings. Moreover,
the willingness to use electricity has a positive relationship with household attributes, such as the number
of occupants, the building size, age and type. In addition, we have found that, other than the availability
of roof insulation, all household attributes have shown a weaker relationship with a households’
willingness to use electricity compared to our control group. Lastly, we have found that the significance
of these household attributes is varying during different hours of the day. This gives reason to believe
that households exposed to dynamic prices have generally become more price sensitive and follow fewer
habitual patterns. Moreover, the analysis of this study has confirmed that dynamic prices have a
significantly different, in our case less positive, relationship with electricity usage behavior compared
to TOU prices. Additionally, our results are able to prove that the capability of households to change
their consumption behavior based on changes in electricity prices only exists between 8AM to 5PM. We
have found that the time window in which household are capable to change their behavior based on
electricity prices is overlapping with the time-window in which the relative usage between treatment
and control group is deviating from each other. We have interpreted this observation as instances of
dynamic pricing encouraged load shifting behavior. In the last step, we have investigated whether the
household attributes reflect lifestyle patterns of households, by conducting a cluster analysis. Our results
show that clusters two and three are showing clearly distinguishable behavioral electricity usage patterns
from the rest of our sample. Two conclusions can be made from these findings. First, by segmenting
households based on their household attributes, we were able to isolate two groups that are potentially
engaging with the introduced dynamic prices by changing their behavioral patterns. We can claim that
cluster 2 reduced their load during the day, while cluster 3 engaged in load shifting behavior. Second,
we can claim that our cluster analysis confirms our assumption that household attributes reflect
electricity usage lifestyle patterns.
4
Master Thesis – Navid Sadat-Razavi
Table of Contents
Executive Summary ............................................................................................... 4
1. Introduction ..................................................................................................... 8
1.1 Context Motivation ............................................................................................................... 9
1.2 Research Question .............................................................................................................. 10
1.3 Structure.............................................................................................................................. 10
2. Theory and Model .......................................................................................... 12
2.1 Demand Response ............................................................................................................... 12
2.2 Consumption Behavior and Behavioral Change ................................................................ 15
2.3 Summary of Concepts ......................................................................................................... 17
2.4 Household characteristics and Household Willingness to Use Electricity ........................ 18
2.5 Electricity Prices and Household Behavioral Consumption Patterns................................ 20
2.6 Household Segmentation for DR improvements ................................................................. 21
3. Methodology and Results .............................................................................. 22
3.1 Data and Descriptives ........................................................................................................ 22
3.2 Analysis of Willingness to Use Electricity (RQ1) ............................................................... 26
3.3 Analysis of Relative Electricity Usage (RQ2) ..................................................................... 35
3.4 Analysis of Household Segmentation (RQ3) ....................................................................... 43
4. Discussion ........................................................................................................ 52
4.1 The Influence of Household Attributes on Willingness to Use Electricity .......................... 52
4.2 The Influence of Dynamic Prices on Usage Behavior ........................................................ 53
4.3 Clustering Usage Behavior Based on Household Attributes .............................................. 54
4.4 Bringing the Findings Together .......................................................................................... 55
4.5 Limitations and Recommendations for Future Research ................................................... 56
5. Conclusion....................................................................................................... 58
5.1 General Conclusion ............................................................................................................ 58
5.2 Managerial Implications..................................................................................................... 59
5.3 Academic Implications ........................................................................................................ 59
6. References ...................................................................................................... 61
7. Appendix .......................................................................................................... 66
7.1 Qurrent Energie Dashboard Screenshots ........................................................................... 70
5
Master Thesis – Navid Sadat-Razavi
List of Tables
Table 1. Incidence of Household Attributes in Reviewed Studies and their Impact on Energy
Consumption .......................................................................................................................................... 19
Table 2. The Real Time Electricity Price Composition of ‘Qurrent Energie’ ...................................... 23
Table 3. Summary Statistics of Household Attributes – Treatment Group .......................................... 25
Table 4. Summary Statistics of Household Attributes – Control Group ............................................... 25
Table 5. Summary Statistics of Willingness To Use Electricity ........................................................... 29
Table 6. Correlation Matrix ................................................................................................................... 29
Table 7. Panel Data Regression Results RQ1 ....................................................................................... 31
Table 8. Testing Influence of Household Attributes between Treatment and Control Group .............. 34
Table 9. Comparison of Regression Results ......................................................................................... 39
Table 10. Statistical Significance of Differences in Regression Coefficients between Treatment and
Control Group ........................................................................................................................................ 40
Table 11. 24h Panel Data Regression Results of Dynamic Prices ........................................................ 42
Table 12. PCA Results .......................................................................................................................... 46
Table 13. Summary Statistics of Prices and Relative Usage................................................................. 66
Table 14. Summary Statistics of Prices and Relative Usage................................................................. 66
Table 15. 24h Panel Data Regression Results Equation 1 – Treatment Group ..................................... 67
Table 16. 24h Panel Data Regression Results Equation 1 – Control Group ......................................... 68
Table 17. PCA Analysis of All Available Household Variables .......................................................... 69
6
Master Thesis – Navid Sadat-Razavi
List of Figures
Figure 1. Classification of DR Programs (Albadi & El-Saadany, 2008) .............................................. 13
Figure 2. Heuristic model of environmentally relevant behavior (Matthies, 2005). Translated by Fisher
(2008). .................................................................................................................................................... 17
Figure 3. Comparing the Willingness to Use Electricity between Treatment and Control Group ....... 32
Figure 4. Comparison of Regression Results between Treatment and Control Group ......................... 33
Figure 5. Dynamic Pricing Scheme with Min and Max. ...................................................................... 37
Figure 6. TOU Pricing Scheme. ............................................................................................................ 38
Figure 7. The Average Relative Load Profiles of Treatment and Control Group. ............................... 38
Figure 8. and 9. Within Group SSE of Actual and 250 Randomized Data Sets against 15 Cluster
Solutions ................................................................................................................................................ 45
Figure 10. and 11. The difference of Within Group SSE of Actual and 250 randomized Data sets
against 15 Cluster Solutions................................................................................................................... 46
Figure 12. Visualization of Five-Cluster Solution of K-Means Analysis Along the Two Strongest
Principal Components ............................................................................................................................ 47
Figure 13. Overview of the Distribution of Clusters ............................................................................ 48
Figure 14. Average Household Occupancy for Each Cluster ............................................................... 49
Figure 15. The Distribution of Building Types for Each Cluster ......................................................... 49
Figure 16. Average Building Size for Each Cluster.............................................................................. 50
Figure 17. Distribution of Terrain Type per Cluster ............................................................................. 50
Figure 18. Differences in the Relative Electricity Usage Across the Identified Clusters ..................... 51
7
Master Thesis – Navid Sadat-Razavi
1. Introduction
The international energy outlook (IEO) 2016 has forecasted a sharp increase in demand for energy until
2040. According to the IEO, the total world consumption of energy has been, and will further increase
from 549 quadrillion British thermal units (Btu) in 2012 to 815 quadrillion Btu in 2040 (U.S. Energy
Information Administration, 2016). The increasing shortage of natural resources such as oil, the
increasing environmental pollution and related threats of global climate change have triggered a new
debate about the sustainable nature of natural resources, and policies to restructure the energy sector.
One specifically disrupted energy sector is the electricity industry. Several utility providers throughout
Europe are experiencing financial losses due to new market conditions (The Economist, 2013). Coming
from a historical position of a quasi-regulated electricity market, it was the responsibility of utility
providers to ensure a stable state of the grid and a reliable supply of electricity in the country. Hence,
the focus of utility providers lied primarily on operational excellence and the security of electricity
supply, creating an inflexible attitude towards dependent households (Spiegel, 2014). Developments of
recent years have changed the electricity landscape, and thereby the success of the traditional business
model of utility providers, namely, the EU policy led energy transition and changing electricity
consumers.
As a result of the policy-led energy transition towards renewable electricity, the European electricity
landscape is rapidly transforming into an industry that makes it difficult for conventional utility
providers to remain profitable. The growing share of renewable electricity in the European energy mix
is increasing uncertainties in electricity production planning (Capgemini Consulting, 2015). Renewable
electricity sources are, unlike traditional sources, volatile in nature and strongly dependent on external
factors such as weather conditions (DeMeo et al., 2007). Hence, renewable energy production has
become increasingly difficult to forecast, due to the dependency on weather conditions.
As electricity consumers are becoming increasingly IT-savvy and used to interactive, smart digital
services from other industries, several new trends are evolving in respect to customer demands towards
utility providers (Capgemini Consulting, 2015). First, customers are looking for more flexible and
responsive suppliers of energy (Capgemini Consulting, 2015). Today, consumers interact with utility
providers approximately 9 minutes per year, which is a considerably small window of interaction
(Accenture, 2015). Second, customers are increasingly interacting with their energy ecosystem, thereby
raising the need for a two-sided communication with utility providers. New renewable energy
technologies like PV-solar panels are enabling households to produce their own energy, making them
‘Prosumers’ rather than consumers (Grijalva & Tariq, 2011). The increasingly decentralized production
of energy will make households more independent from utility providers (Grijalva & Tariq, 2011).
Therefore, utility providers need to reposition themselves by identifying value-added services for energy
consumers that can address one or more issues evolving from the energy transition.
Consequently, utility providers need to focus on process optimizations and the balancing of energy
generation levels on the one hand, and the improvement of customer relationships on the other hand.
Our study is going to focus on new business models that can evolve through a better communication and
relationship with electricity consumers.
8
Master Thesis – Navid Sadat-Razavi
1.1 Context Motivation
The energy transition trend is coinciding with the digital revolution, another significant trend that is
spanning across various industries (Capgemini Consulting, 2015). The emergence of smart technologies,
big data analytics, and information technology have created new means of organizing operations and
customer relationships (Westerman et al., 2014).
1.1.1 The Smart-Grid
These technological developments have a significant impact on the European electricity grid, providing
new possibilities in the energy sector. Smart devices have started to connect residential households with
utility providers and the rest of the grid, producing big amounts of previously unavailable data. This
increased amount of data is enabling new entrants as well as incumbents to reshape internal operations,
gain new insights about market conditions, renewable electricity production and consumer behaviors in
nearly real-time (Capgemini Consulting, 2015). Hence, the notion of the smart-grid provides an entirely
new perspective on the challenges of the electricity industry. The digitalization of energy services can
improve utility providers’ understanding of increasingly uncertain energy production and consumption,
while encouraging a better way of communication with electricity consumers and providing electricity
consumers with new information about their use of electricity.
In the past, several initiatives have been targeted towards residential households as end-consumers of
electrical energy. These so called ‘Demand Side Management’ programs have been partly successful,
but had their limitations due to lack of the resources to properly record consumers’ electricity
consumption behaviors (Capgemini Consulting, 2015). However, recent technological developments
have brought a new perspective to this topic that significantly contributed to our motivation to
investigate the difficulties faced by the energy sector.
1.1.2 Demand Side Management and Demand Response
With changing customer needs, utility providers need to understand how the demand side of the grid can
be managed and supported. Demand Side Management (DSM) refers to a set of measures that can be
used to optimize the demand side of the energy system (Palensky & Dietrich, 2011). DSM ranges from
the improvement of devices and materials in households to improve energy efficiency, up to real-time
adjustments in demand patterns through improved communication technologies (Palensky & Dietrich,
2011). Part of the later is the concept of Demand Response (DR) programs. Essentially, Demand
Response (DR) is building upon the behavioral traits of energy consumers, by offering incentives to
change or shift electricity usage by communicating with energy users in a comprehensive manner
(Darby, 2012). The communication of electricity prices with energy consumers can contribute to
increased knowledge of households to comprehend the electricity market situation.
The development of smart metering devices has opened a new world of possibilities to the energy sector.
These devices provide detailed information about the customers of utility providers, and their energy
consumption patterns (Borenstein et al., 2002). Moreover, the introduction of smart technologies, like
smart meters and smart appliances to households has provided consumers with enhanced abilities to
control and supervise their consumption patterns in a systematic manner (Jacobsson & Bergek, 2004).
Additionally, it has become increasingly easy to reach out to individuals in order to enhance a two-way
communication. Hence, the development of smart devices, and smart meters in specific, has opened up
9
Master Thesis – Navid Sadat-Razavi
new opportunities for utility providers to appropriately reach out to consumers and understand their
behavior.
Utility providers are starting to introduce dynamic prices to their energy contracts with residential
households, with the aim to accurately match fluctuating energy supply with increasingly uncertain
demand patterns. Due to the introduction of smart metering devices, this has become realizable.
However, in order to properly take advantage of this improved way of communication, it has to be
understood how different types of households react to dynamic prices. Our findings will help utility
providers to understand which households are likely to react to dynamically changing electricity prices,
and during what time of the day the electricity prices are an effective tool to influence electricity demand.
In order to fully understand how next generation DR programs have to be designed, the energy sector
needs to obtain a more comprehensive overview of the influence DR components such as dynamically
changing electricity prices have on behavioral consumption patterns of households.
1.2 Research Question
Demand Response programs assume perfectly rational behavior of consumers. However, consumers in
the real world often lack information processing due to limited information (Hermsen et al., 2016). The
introduction of smart metering devices is providing end-users of electricity with more extensive
information about their own electricity usage, and more detailed information about the current electricity
market situation through market-based dynamic electricity prices. The newly available information is
likely to increase consumer informedness and encourage reflective and rational decision making, for
those consumers capable of taking advantage of these information. As a consequence, consumer patterns
of some households are likely to change in the future. It is the aim of our study to investigate these
changing consumer patterns, in order to understand how energy services in dynamic pricing settings
need to be arranged.
Therefore, our research question is as follows:
How can utility providers identify customer patterns for energy services in a dynamic price setting?
1.3 Structure
The theory section is going to clarify the underlying concepts used for this study. Furthermore, we will
provide a conceptual overview of Demand Response programs, explain why we will focus on Real Time
Pricing Demand Programs in specific, and in which relation this program stands compared to the
behavioral theories.
Following the theory section, the Model section is going to provide an in-depth discussion about the
academic work done on our specific subject. Afterwards, a conceptual model and a set of Hypotheses is
presented that we wish to answer during the course of our study.
Furthermore, we will present our research setting and the statistical methods used in this study. Our data
set was obtained from a pilot project of a Dutch energy supplier called ‘Qurrent Energie’, in which
households were (and still are) provided with dynamic electricity prices. We are using a set of panel data
regressions, a principal component analysis, and a k-means analysis to derive at our results.
10
Master Thesis – Navid Sadat-Razavi
We will discuss our findings independently, and explain their value when combining all parts. Lastly, a
concluding part will bring all aspects of our study together and provide a short overview of the most
essential parts of this research.
11
Master Thesis – Navid Sadat-Razavi
2. Theory and Model
2.1 Demand Response
2.1.1 Context
Electric utilities and power network companies have been pushed to reorganize their operations from
vertically integrated mechanisms to open market systems (Bhattacharya et al., 2001). Additionally, the
European Union enforced climate and energy-related policies expects all members to produce 20% of
all generated energy from renewable resources (European Parliament, Council of the European Union,
2009). Due to the volatile nature of renewable resources, the uncertainties in energy generation are
increased. Furthermore, energy consumption patterns have become increasingly uncertain, as the
lifestyle of households is diversifying (Capgemini Consulting, 2015). Part of the change in consumer
patterns are newly formed micro-grids, local energy grids of a small groups of households or a
community capable to operate anonymously. Micro-grids are introducing the capability of households
to self-generate electricity and directly communicate with the rest of their community in order to
optimize the use of renewable electricity. Micro-grids have added a new level of complexity to the
electricity environment of residential household, and require detailed information about electricity
generation and use in order for households to be useful. The introduction of smart metering devices to
the smart grid enables utility providers to supply the demanded information to households through
informational electricity usage feedback on the one hand, and dynamic electricity prices on the other
hand. Informational electricity usage feedback is capable of informing households about their
consumption patterns, while a better overview of electricity prices enables households to gain a better
understanding of the electricity market situation.
Demand Response programs are a cheap alternative for balancing the electricity systems and have
received increasing attention due to new technology-enabled ways to communicate with electricity endusers (Torriti et al., 2010).
2.1.2 Definition
Demand Response (DR) refers to a wide range of actions that can be taken on the customer side of the
electricity meter, in order to respond to specific conditions within the electricity system (Torriti et al.,
2010). Essentially, Demand Response (DR) is building upon the behavioral traits of energy consumers
by offering incentives to change or shift electricity usage (Darby, 2012). More specific, demand response
can be defined as deviations from the usual electricity usage of a residential household in response to
changing electricity prices over time (Albadi & El-Saadany, 2008).
Program Classifications
The different DR programs can generally be classified into two main categories, namely Incentive-Based
Programs (IBP) and Price-based Programs (PBP) (Albadi & El-Saadany, 2007). Figure 1 visualizes the
categorization of different DR programs.
12
Master Thesis – Navid Sadat-Razavi
Incentive-Based Programs (IBP)
Incentive-Based Programs (IBP) are focused on providing an actual,
direct incentive to the participating customers. Moreover, IBPs can
be subdivided into Classical, and Market-Based IBPs. Classical
IBPs provide customers with participation payments for their
participation in the Program. For instance, residential households
and small commercial customers can sign up for programs in which
utilities
are
capable
of
remotely
shutting
down
specific equipment on demand (direct control) or give their
consumers specific load targets to adhere to (interruptible /
curtailable programs). Furthermore, in market-based IBPs
participants are rewarded money for the performance towards preset
targets of the utility provider. Demand bidding allows participating
Figure 1. Classification of DR
customers to bid load reduction on the electricity wholesale market,
Programs (Albadi & El-Saadany, 2008)
while Emergency DR programs are providing financial rewards for
measured load reductions during emergency situations.
Furthermore, Capacity Market Programs offer capacity payments for customers willing to restrain from
electricity consumption when directed. Lastly, the Ancillary Services Market Programs offer customers
the opportunity to offer load reductions in the intraday energy markets (Contreras et al., 2016).
Priced-Based Programs (PBP)
In PBP programs, consumers are offered dynamic pricing rates over time. In PBP programs electricity
prices are significantly higher during peak-periods than during off-peak periods. Time of Use (TOU)
pricing is changing the unit price of electricity during specific time periods at a fixed rate. Moreover,
Critical Peak Pricing (CPP) is imposing a premium on electricity during special peak times, while
Extreme Day Pricing (EDP) is imposing a premium on electricity during special high-demand days.
Logically, Extreme Day CPP (ED-CPP) is a combination of both, in which electricity prices are
increasing during special peak times on high-demand days. Lastly, in Real Time Pricing (RTP) schemes,
customers are informed about, mostly hourly changing, varying electricity prices on a day-ahead or
hour-ahead basis (Contreras et al., 2016). It has been widely agreed that RTP programs are the most
effective DR programs for electricity markets as they are able to effectively communicate real market
situations of electricity markets (Bloustein, 2005). Moreover, given the recent technological
developments and an advanced metering infrastructure in Europe, effective communication of prices
and detailed load profiles between utility providers and electricity consumers have become possible.
Dynamic pricing schemes are usually based on retail prices and reflect real-time system costs, thus,
encouraging energy consumers to reduce or shift energy consumption during high wholesale price
periods. Consequently, dynamic prices in a Real Time Pricing (RTP) scenario provide the best available
information about the marginal value of electrical energy at a location, during a specific point in time.
In order to successfully apply dynamic prices on a broad scale, households need to be provided with
hourly meters capable of recording and communicating a household’s electricity usage per hour
(Contreras et al., 2016).
13
Master Thesis – Navid Sadat-Razavi
The APX Electricity Market
The APX (NL) electricity market is an independent exchange for electricity trading on the spot market.
Distributors, producers, traders and industrial end-users can trade on this market for day-ahead
transactions and intra-day transactions (APX Group, 2016). On the day-ahead market, trading is
conducted on the day before the delivery of the traded good. The participating entities can submit their
orders electronically. Based on the submitted data for demand and supply of electricity, the electricity
prices are calculated for every hour of the following day (APX Group, 2016). Hence, the hourly
electricity prices are fluctuating based on the overall markets demand and supply for electricity.
Consequently, this means that the dynamic prices of customers in RTP DR programs are influenced by
usual electricity consumers exposed to TOU prices. This creates a dependency of the two different
electricity consumer types, and implies that as the transition towards the use of dynamic prices
progresses, the pricing structure of day-ahead market based dynamic prices will dramatically change.
Customer Response
Three general reactions of customers as a response to DR programs have been observed (US Department
of Energy, 2006). First, energy consumers can engage in ‘Peak Shaving’, the reduction of electricity
during critical peak periods, when prices are high. Second, energy consumers might engage in ‘Load
Shifting’, by shifting part of their energy consuming activities to times of lower prices. Lastly, energy
consumers can differ from their usual energy consumption patterns by engaging in on-site generation of
electricity. Although the actual behavior might not change with the latter response type, from the utility
perspective energy usage patterns of these consumers will change significantly (Albadi & El-Saadany,
2008).
Overview
Demand Response programs assume perfectly rational behavior of consumers. However, consumers in
the real world often lack information processing due to limited information (Hermsen et al., 2016). The
introduction of smart metering devices is providing end-users of electricity with more extensive
information about their own electricity usage, and more detailed information about the current electricity
market situation through market-based dynamic electricity prices. The newly available information is
likely to increase consumer informedness and encourage reflective and rational decision making, for
those consumers capable of taking advantage of these information. As a consequence, consumer patterns
of some households are likely to change in the future.
The underlying question of DR programs is why some households are inelastic to electricity prices,
while others are likely to respond. Another question is how varying levels of price sensitivity can be
explained. The consumer choice theory and the theory of habitual behavior are aiming to explain rational
decision making and irrational decision making in the form of habits, respectively. Both theories are
explained in section 2.2. Subsequently, the scope of our study is going to be explained based on these
theories and the concept of DR programs.
14
Master Thesis – Navid Sadat-Razavi
2.2 Consumption Behavior and Behavioral Change
2.2.1 Consumer Choice Theory
Consumer choice theory presents a set of principles that are able to explain consumers’ decisions to buy
a given product (Deaton & Muellbauer, 1960). More specific, the theory aims to clarify the relationship
between consumer preferences and time- and budget-constraints, in order to explain how consumers
arrive at a final product decision (Deaton & Muellbauer, 1960). Consumer preferences allow consumers
to weight different sets of goods according to the total satisfaction of consuming that product or good
(Deaton & Muellbauer, 1960). Consumers making choices are generally exposed to financial- and timerelated constraints that limit their ability to freely pick a product based on sole satisfaction (Salvatore,
2008). Financial constraints contain income constraints, the extent to which the relative buying power
of one consumer changes towards a given product, and budget constraints, the general change in price
for a product. Moreover, time-related constraints display the fact that consumers are not capable of freely
choosing when to consume a product.
Taking this concept into the energy sector, varying income levels of household will generally lead to
high-income households to consume more energy than low-income households, according to consumer
choice theory. However, the preferences of a household to use electricity during a specific hour of the
day can increase the price elasticity of that household during that time. Moreover, in a scenario with
hourly changing electricity prices, electricity at 5PM can be seen as a substitute product to electricity at
6PM or 7PM. However, as storage options are currently limited and not widely implemented, a timerelated constraint is influencing the substitution effect. Households that have to consume electricity at
the time of purchase are not capable of substituting electricity at 7PM entirely for electricity at 5PM,
they are merely able to shift part of their consumption patterns to a financially more satisfying time.
Nevertheless, the consumer choice theory reveals that dynamically changing electricity prices should
theoretically encourage consumers to shift part of their electricity consumption to times in which
electricity is cheap. Lastly, it is worth mentioning that the height of the price variations plays an
important role when applying the consumer choice theory to the energy sector (Schleich & Klobasa,
2013). While modest price variations will only attract households with lower incomes, higher price
variations should be capable of attracting a bigger group of households.
The consumer choice theory displays how purchasing decisions are made at the individual level.
According to this theory, it is reasonable to assume that consumers will react to electricity price
variations over time. Additionally, the possibility to see the price development in advance should
encourage some households to exchange electricity during high-price times with electricity at low-price
times. Moreover, the above mentioned effect is likely to change its strength with different income levels,
and during different times of the day. More specific, time-related constraints will enforce the need for
certain amounts of electricity to be consumed during specific moments of the day, which leads to
inconsistent price sensitivities of individuals during a day.
Much of the time-related constraints and the earlier mentioned price-inelasticity explained in the
consumer choice theory can be regarded as habitual patterns of households. Although much of the
electricity consumed at highly demanded times might be reducible or shiftable, daily routines and
lifestyle patterns and switching costs prevent individuals from doing so. It is therefore necessary to
15
Master Thesis – Navid Sadat-Razavi
understand how habits lead to irrational decision making and how these patterns can be identified and
broken by individuals, in the context of household energy consumption.
2.2.2 Habitual Behavior
Several approaches to creating a psychological model of energy consumption behavior as a basis for
successful behavioral change have been developed. However, one model has been especially successful
in forming a heuristic model of environmentally relevant behavior, by reviewing theory and findings
from the entire discipline. This integrated model of environmentally relevant behavior is helpful in
explaining why and how energy consumption related feedback influences individuals’ behaviors
(Fischer, 2008; Matthies, 2005). The model by Matthies (2005) is displayed in Figure 2 below and
differentiates between two general types of actions.
First, habits (environmentally detrimental habits) are actions that are not reflected upon and are
performed similarly on a regular basis. Second, conscious decisions (the area above environmentally
detrimental habits) are active decision making processes that can break habits through new evaluations
of an individual’s values (Fischer, 2008).
By ensuring that individuals are provided with information capable of showing them that their actions
are leading to an environmental problem and that this problem can be resolved by behavioral change,
environmentally detrimental habits can be broken and replaced. This process is called ‘Norm Activation’
(Fischer, 2008). After the norm activation process, an individual proceeds to a process of evaluating
different motives on how to act. In general, the heuristic model of environmental behavior distinguishes
three motivations; personal environmental motives, social motives (expectations of others), and other
motives (e.g. costs of new behavior). After weighing the importance of these three motives individually,
a person proceeds by performing a more- or less environmental friendly actions. Although not
specifically mentioned, the introduction of new information as a reminder of current behavioral patterns
is necessary for an individual to break habitual behaviors (Fischer, 2008).
Therefore, the delivery of energy consumption feedback and information as a reminder of current
behaviors, such as electricity prices, fills an ‘information vacuum’, which enables them to react and
make more informed choices (Buchman et al., 2014). Naturally, consumers can have varying preferences
for the type of feedback delivered to them, and might show varying levels of responsiveness.
Consequently, the context in which feedback is delivered is equally important as the message itself.
Taking the daily routines of households as an example, the same energy consumption feedback, such as
the electricity price, delivered to a household might find varying levels of attention and acceptance
during the afternoon, as opposed to the early morning or evening times, where other activities might be
prioritized.
Information and feedback will only be significantly influencing energy consumption behavior in
households that deem a specific form of information as relevant and valuable. Additionally, this insight
emphasizes the importance of the context, and the type of person a piece of information is delivered to.
16
Master Thesis – Navid Sadat-Razavi
Figure 2. Heuristic model of environmentally relevant behavior (Matthies, 2005). Translated by Fisher (2008).
2.3 Summary of Concepts
The electricity consumers’ buying choice is dependent on personal preferences and budget- and timeconstraints (Deaton & Muellbauer, 1960). Consequently, different types of households will base their
evaluations whether to buy electricity or not on different personal preferences, electricity prices, and
time-constraints that could potentially enforce them to make a rational decision. Additionally, behavioral
energy consumption patterns of households can depend on habits and routines of households that are
performed unconsciously (Fischer, 2008). In order for the energy consumers to break habitual behavior
and change behavioral energy consumption patterns, they need to be more informed about dynamic
pricing mechanisms (Matthies, 2005). Real Time Pricing Demand Response Programs (RTP DR) are
building upon these behavioral aspects of energy consumers by communicating hourly dynamic prices
that reflect the real marginal cost of electrical energy of the system (Contreras et al., 2016). By providing
hourly changing prices for electricity to residential households, energy consumers have the increased
ability to make buying decisions based on their preferences, budget- and time-constraints. However, the
willingness to use electricity might heavily depend on households’ traits as preferences and preferred
time of electricity usage are likely to vary. Furthermore, as the hourly dynamic prices in RTP DR
programs are often delivered on a day-ahead basis, it is likely that some households are willing to break
their habitual behaviors or change their behavioral patterns due to the constant exposure to their
electricity usage behavior. For the same reason, behavioral electricity usage patterns of some individuals
are likely to have changed due to the exposure to dynamic prices. Additionally, the evaluation of
preferences and budget- and time-constraints of these households are likely to vary during different
hours of the day, giving reason to believe that dynamic prices in RTP DR Programs might not
successfully influence energy consumers’ behavioral patterns throughout the day. Lastly, if
households’ attributes indeed influence energy consumers’ willingness to use electricity, and dynamic
prices are only successfully influencing electricity usage patterns during specific times of the day, utility
providers would be well advised to segment their consumer base, in order to improve their understanding
of customer patterns.
The following sections are going to deepen the discussion about the current academic findings of three
main topics of interest. Namely, the influence of household attributes on the willingness to use electricity
in dynamic pricing environments, the influence of dynamic prices on behavioral electricity usage
patterns, and the segmentation of electricity consumer groups based on household attributes.
17
Master Thesis – Navid Sadat-Razavi
2.4 Household characteristics and Household Willingness to Use Electricity
Household attributes have been proven to play an important role in explaining how willing energy users
are to conserve energy (Nababan, 2015). Moreover, next to the influence of electricity prices on absolute
electricity consumption, the effect of building characteristics and socio-demographic information have
been a thoroughly studied field (Hayn et al., 2014; Schleich & Klobasa, 2013). In their study on Timeof-use (TOU) pricing and residential electricity demand in Germany, Schleich and Klobasa (2013) have
found that the size of a households building is positively influencing energy consumption, while the
number of appliances positively influences the electricity consumption in the period from May to
October (Schleich & Klobasa, 2013). Furthermore, another study has found that the number of occupants
of a household, the building size of a household, the building type, and the number of bedrooms
positively influence the electricity consumption (Yohannis et al., 2008). Additionally, a study
characterizing domestic electricity consumption patterns in Ireland has found that the number of
occupants, the building size and the building type are positively influencing residential electricity
consumption (McLoughlin et al., 2012).
However, contradicting findings have also been reported in other studies. A study investigating
determinants of residential electricity consumption has found the number of occupants and the building
size to be positively associated with electricity consumption, while the building type and the building
age are not associated with electricity consumption (Kavousian et al., 2013). Furthermore, a study
investigating short- and long-run price elasticities has found that the income is positively influencing
peak and off-peak residential electricity consumption, while the household size does not show any
significant influence (Filippini, 2011). Additionally, another study has found that the building type of a
household is not influencing electricity consumption, while the building age has a direct influence over
the electricity consumption (Statistik Austria, 2011).
Moreover, it is vital to note that the previously mentioned studies investigated the influence of household
attributes in a flat or time-of-use pricing environment, and not in a dynamic pricing environment.
However, a recent study investigating the influence of the building size and building type has found that
the building size does not show a significant relationship with electricity consumption, while the
building type has shown a significant influence (Alberini et al., 2011). Unfortunately, this study did not
make an attempt to understand if the role of building characteristics and socio-demographic information
is significantly different compared to TOU pricing settings. Additionally, our study aims to complement
the recent studies investigating dynamic price settings by investigating what type of different groups
and behaviors exist. Lastly, we contribute to the current academic findings by investigating the influence
of household attribute on an hourly basis, which can improve the targeting of households if certain
household attributes show not to be significant during each hour of the day.
Consequently, the partly contradicting results of former studies need to be reevaluated. One plausible
explanation could be that the influence of building characteristics and socio-demographic information
is varying depending on the time of the day, country under examination, and the type of the pricing
mechanism. Another possibility is that different pricing environments influence the previously
mentioned relationship. The aim of this study is to cover the currently persistent gap in the academic
literature.
The following Research Question (RQ1) arises from our discussion:
18
Master Thesis – Navid Sadat-Razavi
How do household-level attributes influence a household’s willingness to use electrical energy
throughout the day?
With the following Hypotheses:
H1: The attributes of a household significantly influence the willingness of a household to use electrical
energy.
H1a: The number of occupants in a household has a significant positive relationship with the willingness
of a household to use electrical energy.
H1b: The age of a household’s building has a significant positive relationship with the willingness of a
household to use electrical energy.
H1c: The size of a household’s building has a significant positive relationship with the willingness of a
household to use electrical energy.
H1d: The type of a household’s building has a significant influence on the willingness of a household to
use electrical energy.
H1e: The roof insulation type of a household’s building has a significant positive relationship with the
willingness of a household to use electrical energy.
H2: The relationship between the attributes of a household and the willingness of a household to use
electrical energy is weaker in the dynamic pricing environment compared to the TOU pricing
environment.
Source
Variables of Interest
No.
Occupants
Building Size
Schleich and Klobasa (2013)
+
Yohannis et al. (2008)
+
+
McLoughlin et al. (2012)
+
+
Kavousian et al. (2013)
+
+
Filippini (2011)
Statistik Austria (2011)
Alberini et al. (2011)
Building Age
insignificant
Building Type
+
No. Appliances
+
+
No. Bedrooms
+
+
Composition
+
Income
insignificant
insignificant
+
+
-
insignificant
Other
insignificant
+
+ = positive influence on energy consumption
- = negative influence on energy consumption
insignificant = no influence on energy consumption
Table 1. Incidence of Household Attributes in Reviewed Studies and their Impact on Energy Consumption
19
Master Thesis – Navid Sadat-Razavi
2.5 Electricity Prices and Household Behavioral Consumption Patterns
Previous studies have convincingly suggested that dynamic pricing strategies would encourage the
price-responsive demand to balance supply and demand of electricity (Borenstein et al., 2002). The first
ever hourly Real Time Pricing (RTP) program for residential customers, however, was only conducted
in 2011 in Chicago. The results of the demand estimates of the respective study suggested that dynamic
prices only influence the residential electricity demand during peak hours. More specific, it was found
that participating residential households were engaged in peak shaving behavior, but not in load shifting
behavior (Allcott, 2011). Other studies examining electricity prices have found similar results, indicating
that electricity prices only affect peak demand not off-peak demand (Schleich & Klobasa, 2013).
Additionally, a study estimating the impact of time-of-use (TOU) pricing on Irish electricity demand
has found that, while prices are affecting peak demand, they are not triggering load shifting behavior
(Di Cosmo et al., 2014).
To the contrary, another paper investigating the impact of TOU pricing on electricity consumption has
found that peak and off-peak consumption are negatively affected by increasing electricity prices
(Filippini, 2011). Additionally, another study has found that demand based TOU electricity tariffs have
decreased peak demand and shifting electricity demand from peak to off-peak periods (Bartush et al.,
2011).
Next to widely contradicting results of the influence of electricity prices on electricity demand, Di
Cosmo et al. (2014) have found significant evidence that the influence of electricity prices is different
across household groups (Di Cosmo et al., 2014).
Several implications can be made from the earlier mentioned findings. First, it is likely that energy usage
related behavioral patterns of households are affected by dynamic prices, because the constant update
with prices should increase household informedness and awareness of energy usage, and should
therefore potentially break habits and previous consumption patterns. Second, it is reasonable to assume
that dynamic prices are having a significantly different impact on the consumption behavior of
residential households compared to TOU prices, due to the constant reevaluation of preferences, and
time- and budget – constraints needed. Although this assumption is self-evident, it yet needs to be
proven. Third, given the contradicting findings of electricity price influence on demand, it is possible
that electricity prices are only affecting households during specific time of the day. Or in other words,
it is reasonable to assume that residential households are only capable of changing their behavioral
electricity usage patterns during certain moments of the day. Understanding at what times of the day
households are capable of changing consumption patterns would be vital for the improvement energy
services. Fourth, given the finding that the influence of electricity prices is varying across household
groups, it is vital to explore how households could be segmented.
The following Research Question (RQ2) arises from our discussion:
How do dynamic prices influence a household’s capability to change electricity usage behavior?
With the following Hypotheses:
H3: The dynamic electricity price has a significantly different influence on the relative daily electricity
usage, compared to the TOU electricity price.
20
Master Thesis – Navid Sadat-Razavi
H3a: The dynamic electricity price during the day (08:00-16:00) has no significant relationship with
the relative daily electricity usage during day-times.
H3b: The dynamic electricity price during peak-times (17:00-19:00) has a negative significant
relationship with the relative daily electricity usage during peak-times.
H3c: The dynamic electricity prices encourage the households to shift loads.
2.6 Household Segmentation for DR improvements
As visible from previous elaborations and discussion, studies investigating the effect of electricity prices
on energy consumption behavior have been presenting variations in effect, sizes and significance of
different electricity pricing types. Additionally, studies have argued that the effectiveness of energy
information is often not generalizable across cultures and demographic groups (Fischer, 2008).
Households with higher income, higher education levels, and higher electricity use are more reactive to
energy consumption behavior change than other groups (Wilhite & Ling, 1995; Vine et al., 2013). In
contrast, other feedback studies could not find a clear link between household-level characteristics and
price effectiveness (Brandon & Lewis, 1999). Consequently, the question arises whether specific,
predefined consumer groups have varying levels of capability to react to dynamic prices.
Earlier attempts to appropriately segment energy consumer groups have either focused on usage-based
clustering or on the impact of socio-demographic factors and the equipment with electric appliances and
new technologies (Hino et al., 2013; Kwac et al., 2014; Hayn et al., 2014). A detailed segmentation of
household electricity usage provides the opportunity to better reflect on future energy systems and might
be useful to create new load profiles (Hayn et al., 2014).
Creating household segments based on near-static characteristics can potentially help utility providers
to model households’ electricity consumption and behavioral load patterns (Hayn et al., 2014).
Therefore, it is reasonable to assume that the earlier investigated household attributes are capable of
segmenting electricity consumers with varying levels of capability in simple but an effective manner.
Moreover, cluster analysis is a central element in marketing and widely used for market segmentations
and the identification of consumers with similar needs and behaviors (Hayn et al., 2014). With
increasingly detailed energy consumption and household data available, cluster analysis has received
increasing attention in academic studies of the energy sector. Next to academic contributions discussing
the use of supervised clustering techniques for electricity load profiles and consumption patterns, several
studies have suggested to follow unsupervised clustering techniques, similar to those used in market
segmentation research (Hayn et al., 2014; Hino et al., 2013; Kwac et al., 2014).
The following Research Question (RQ3) arises from our discussion:
How can utility providers sufficiently segment their energy consumers into groups with varying
capabilities to react to dynamic prices?
With the following Hypothesis:
H4: A segmentatin based on household attribtues is capable of identifying different electricity usage
patterns
21
Master Thesis – Navid Sadat-Razavi
3. Methodology and Results
3.1 Data and Descriptives
Our analysis is based on household-level hourly consumption data of the Dutch energy supplier ‘Qurrent
Energie’. The data were collected during the month of March 2016, as part of a pilot project on dynamic
pricing schemes of the company. During the pilot project, some of the participating households were
confronted with hourly electricity prices that were fixed to the electricity price variations of the APX
energy market. All households receiving dynamic electricity prices were exposed to the same price per
hour. In order to assess whether dynamic prices led to a change in household electricity consumption,
the response of the households on the dynamic prices was recorded in hourly blocks, as the net usage of
electricity in kilowatt per hour.
Next to the electricity usage, the participants answered a survey about specific attributes and
characteristics of their households. The results enable us to gain a deep understanding of the differences
and similarities between household groups, as well as the opportunity to investigate the presence of
significantly different household responses to hourly changing prices. In specific, the survey asked the
households about the number of occupants, the type of the house, the insulation of the house, the
location, the size, the heating type, and the use of solar panels.
Additionally, all participating households had access to a web-based dashboard provided by ‘Qurrent
Energie’. This web-based dashboard allows customers of the company to get a better, real-time
understanding of their energy-related activities. The customers of ‘Qurrent Energie’ are able to display
their past energy consumption, to understand their PV panel production, and access the dynamic prices
for electricity, if applicable to the household, through the web application. Moreover, the webapplication of the company can be used via phone, tablet and conventional computers. Screenshots of
the web application can be found in the Appendix in section 7.1.
3.1.1 Sample
Our sample consists of 225 participating households, with 75 households used for the actual pilot project,
and an additional 150 households as the control group. The survey concerning the household attributes
was answered by 73 households of the actual pilot project, and 35 households of the control group,
making the first part of our study (RQ1) less generalizable. All households were exposed to the same
applications, with the only difference being the dynamic pricing scheme that was only shown to the
treatment group. The control group was exposed to a time-of-use electricity price during the time of the
study, and consisted of usual ‘Qurrent Energie’ consumers that were not specifically included in the
project. An overview of all included variables, the reason for their presence and their computation can
be found in the following section.
22
Master Thesis – Navid Sadat-Razavi
3.1.2 Variables
Measurement of Electricity Usage
Absolute Electricity Usage (kWh)
Energy consumption was measured as the ‘Net Electricity Usage’ in kilowatt per hour. The total amount
of energy consumed by a household subtracted by the total amount of energy produced by a household
within an hour, in kilowatt per hour.
Independent Variable – Electricity Price
We have investigated the influence of dynamic and TOU electricity prices on electricity usage. Our
treatment group has received hourly changing electricity prices, while our control group received TOU
electricity prices.
The dynamic electricity prices are the APX electricity market prices plus any additional surcharges that
are generally applicable to electricity end-users. Hence, the final dynamic price of this study is composed
as:
Dynamic Price = (APX market price + BS + ET + SES + GOO) * (1 + VAT)
The TOU electricity prices are composed of a marginal fee of the electricity provider ‘Qurrent Energie’
that reflects the forward price and a risk premium. Moreover, the TOU electricity price changes between
peak- and off-peak times.
Time-Of-Use Price = ((forward price + risk premium) + BS + ET + SES + GOO) * (1 + VAT)
Fee
Description
Amount
Balancing surcharges
(BS)
€0,0071 / kWh
Energy Tax
(ET)
Energy suppliers in the Netherlands are charged with fees for the
use, management, and balancing of the national grid. This amount
is directly forwarded to the households.
Households in the Netherlands are taxed on their energy use per
kilowatt hour.
Sustainable Energy
Surcharge (SES)
An additional tax on household energy consumption, used to
stimulate investments into sustainable energy sources.
€0,0056 / kWh
Guarantee of Origin
(GOO)
Value added tax (VAT)
A fee charged by the Energy supplier to ensure that only energy
from renewable resources is bought for Qurrent households.
€0,0028 / kWh
€0,1007 / kWh
21% of total
Table 2. The Real Time Electricity Price Composition of ‘Qurrent Energie’
Household Attributes
The importance of household attributes as indicators of lifestyle patterns has been described by several
studies investigating household energy consumption profiles (Hayn et al., 2014). More specifically,
household attributes such as the number of occupants, the size of the building, and ownership of
23
Master Thesis – Navid Sadat-Razavi
electrically run household applications can indicate the height of a household’s energy consumption, but
also its flexibility when aiming to decrease or shift energy consumption (Hayn et al., 2014).
Number of Household Occupants - One of the most evident household attributes is the number of persons
living in a household (Hayn et al., 2014). Indications about total energy consumption, and also the
capability to react to prices resulting from the higher probability that someone is inside the house can be
made. The number of household occupants varies between one and six persons in the sample of our
study.
Building Size – The building size of a household can potentially define load shifting capabilities of
households, since the availability of more rooms, lights, appliances etc. enables individuals to decrease
or shift consumption more easily compared to smaller households. The building size is measured as the
total floor size in square meters (m2).
PV Panel Ownership – The ownership of PV panels is significantly influencing the net electricity usage
(DV) of households during times where the sun is shining. Additionally, it is reasonable to assume that
PV panel owners might be less price sensitive to the dynamic prices than non-owners. Hence, it is
obligatory for our study to make a distinction between households that own PV panels, and those that
do not. PV panel ownership is included as a dummy variable, indicating 1 as ownership and 0 as nonownership.
Building Age – The age of a building can indicate the age of its electricity consuming appliances, such
as fridges, freezers and washing machines. Additionally, younger households are likelier to possess
smart technologies and more efficient appliances. The building age is taken into consideration as a
categorical variable with four general categories:
1.
2.
3.
4.
27 years or younger
Between 40 and 28 years
Between 50 and 41 years
51 years or older
Electric Heating – The presence of electrically run heaters is accounted for with a dummy variable,
indicating electric heating with 1, and other types of heating with 0.
Building Type – The building type a household is residing in, partitioned into five categories. The values
of this categorical variable range from one to five.
1.
2.
3.
4.
5.
Apartment (‘Appartement’)
Row House (‘Tussenwoning’)
Detached House (‘Vrijstaand’)
Corner House (‘Hoekwoning’)
Semi-detached House (‘Twee onder een kap’)
Terrain Type – Terrain Type is a categorical variable that indicates whether a household is located in an
‘Urban’, ‘Suburban’, or ‘Rural’ area. The categorization benchmark was set as follows:
1. Rural – less than 1000 inhabitants per km2 at household location
24
Master Thesis – Navid Sadat-Razavi
2. Suburban – between 1000 and 3000 inhabitants per km2 at household location
3. Urban – more than 3000 inhabitants per km2 at household location
Household Attributes – Descriptives
Table three and table four present the descriptive statistics of the household attributes. The average
number of household occupants is 2.63 for the treatment group and 2.97 in the control group. Another
feature in which both the samples differentiate from each other is the maximum number of occupants in
a household, which is six for the treatment group and eight for the control group. The average building
age of the sample of this study ranges between the categories two and three, which is similar to the
control group, between 28 and 50 years. The average building size of the treatment sample is 158.2m2
and comparable to the control group with the minimum household size being 58m2 and the maximum
household size being 550m2. Furthermore, the average building type is ranging between type 3 and type
4, which means that most households were either living in house type ‘Vrijstaand’ or in house type
‘Hoekwoning’. Additionally, 18.5% of all households are using electrically run heating in the treatment
group, while 21.9% of the households in the control group are using electrically run heating. Lastly, the
availability of roof insulation is at 85.3% in the treatment group, while it is at 76% in the control group.
Summary Statistics - Treatment Group
N
Mean
Median
Min
Max
Persons
85
2.63
2
1
6
Building Age
85
2.739
3
1
4
Building Size
85
158.2
140
58
550
Building Type
85
3.416
3
1
5
Heating Type
85
1.185
2
1
2
Roof Insulation
85
1.853
2
1
2
720
37.74
Solar Influx
Table 3. Summary Statistics of Household Attributes – Treatment Group
1
0
259
Summary Statistics - Control Group
Persons
Building Age
Building Size
Building Type
Heating Type
Roof Insulation
N
Mean
Median
Min
Max
35
35
35
35
35
35
2.967
2.239
153.5
3.02
1.219
1.76
3
2
123
3
2
2
1
1
50
1
1
1
8
4
600
5
2
2
1
0
259
720
37.74
Solar Influx
Table 4. Summary Statistics of Household Attributes – Control Group
Weather Variable
Solar Influx – Potential PV production amount and sunshine intensity measured as the Solar influx in
J/cm2. Solar influx was measured as hourly data from the Ministry of Climatology of the Netherlands
25
Master Thesis – Navid Sadat-Razavi
(KNMI). Moreover, solar influx information for taken from 20 different weather stations. Each
household received the solar influx information from the weather station closest to the household.
The data recorded from the pilot project was analyzed in three steps in order to answer the three main
research questions of this study. First, this study will conduct a set of panel data regression analyses with
the aim to understand which household characteristics influence a household’s willingness to use
electrical energy given different price levels (RQ1). Second, this study will conduct another panel data
regression in order to determine how dynamic prices influence the relative consumption of households
throughout the day in order to determine behavioral patterns and potential load shifting capabilities
(RQ2). Third, a principal component analysis and an unsupervised cluster analysis will be conducted
with the aim to sufficiently segment the households into distinguishable groups (RQ3). Lastly, the new
household segments are evaluated in terms of their dynamic price responsiveness and DR
recommendations would be given. The following sections are going to provide a description of the
analysis types used in this study. Further details about the statistical methods can be found in the
respective sections.
3.2 Analysis of Willingness to Use Electricity (RQ1)
3.2.1 Method
Panel Data Regression
Panel data regression is a statistical approach that measures the behavior of entities such as firms,
industries or households and across time (Baltagi, 2013). Unlike time-series or cross-sectional
regressions, a panel data regression has a double-subscript on its variables, enabling us to take into
account cross-sections, as well as time-dimensions during the analysis (Baltagi, 2013). A panel data
regression is denoted as:
Yi,t = αi + X’i,t β + εi,t
i = 1 …. N;
t = 1 …. T
with i denoting an observed entity, such as countries, firms or individuals, and t denoting time. Hence,
the i subscript denotes the cross-sectional dimension, while the t subscript denotes the time dimension.
Moreover, α is a scalar, X’I,t is the ith observation of explanatory variable X’ at time t. Furthermore, β
denotes the coefficient estimate, or the main effect, of explanatory variable X on the dependent variable
Y (Baltagi, 2013). Lastly, ε is an error term of the model, which captures variation in the dependent
variable not explained by the independent variables.
Additionally, this study performs a Hausman test, in order to understand whether random effects or fixed
effects need to be used for the panel data regression. This approach ensures the sufficient measurement
of the interrelatedness of the variables of this study, while accounting for individual differences across
households and time. The Hausman test is denoted by the following formula:
H = (βFE − βRE)′[Var(βFE) − Var(βRE)]−1(βFE − βRE)
Where βFE is the fixed effects estimate and βRE is the random effects estimate. The null Hypothesis is
claiming that β is consistent across our panels. Therefore, fixed effects estimates have to be used in case
H0 appears to be true, and random effects should be used in case we can reject the null Hypothesis.
26
Master Thesis – Navid Sadat-Razavi
Controlling for fixed effects obliges us to explore the relationship between the proposed individual and
outcome variables within a household. Given that household level attributes have the same level of
influence on the dependent variable or if omitted variables are included in the regression, controlling for
fixed effects would be recommended for the panel data regression. A random effect model assumes that
entity error terms are not correlated with the independent variables (Greene, 2008).
Assuming that each household has his very own price sensitivity and energy usage patterns, a panel data
regression is capable of assessing the statistical significance of the individual variables within each
separate panel. More specifically, panel data analysis allows a study to account for individual-level
heterogeneity, making it possible to control for variables that are not measurable per se across
households or across time (Baltagi, 2013). Hence, this type of regression analysis is capable of analyzing
the change in demand response of each household separately over time. Panel data regression analysis
has been used to examine Research Questions one and two of our study. Further details about the exact
equations can be found in the respective sections.
Data Preparation
In order to answer the first research question, the collected hourly consumption data were merged with
household-level data from the questionnaires and subsequently split into 24 separate data sets, one data
set for every hour of the day, for the panel data regression analysis. Consequently, 24 separate panel
data regressions were conducted for the data, in order to assess the relevance of the influence of
household attributes on the price sensitivity of households for every hour of the day.
Equation 1
In order to investigate the influence of household attributes on the price sensitivity of households for
every hour of the day, 24 separate panel data regression analyses have been conducted. This approach
enables us to make statements about the significance of specific household characteristics throughout
the day. By understanding how the influence of household attributes on household willingness to use
electricity is changing, we are able to explain which households are likely to respond to price signals
during a specific moment in time. The following dependent variable was computed to represent the
‘willingness of households to use electricity’:
Willingness to Use Electrical Energy (Price sensitivity)
WTU = Usagei,t / Pricet
The reason for the computation of our dependent variable is to factor out the influence of the price on
the absolute usage of electricity. In this way, our study is capable to compare the absolute usage between
our treatment and control group in a more sophisticated manner. Hence, changes in consumption
behavior that have occurred due to the introduction of dynamic prices, such as load shifting behavior,
can be controlled for. A central assumption of our dependent variable is that the entire load of a
household is elastic, which is not the case in reality. In fact, only a fraction of a household’s load is
elastic to prices, making it necessary to collect more data about the household’s appliances, in order to
understand the percentage of elastic load each household has. We have elaborated on this issue in our
limitations in section 5.2.
27
Master Thesis – Navid Sadat-Razavi
The hypotheses one and two are covered by this equation. This study derives the following equation for
the above-mentioned analytical approach.
Equation 1: Investigating the Influence of Hourly Dynamic Prices on the Electricity Usage of
Households During Different Times of the Day
Y WTUi,t = αi + β1*occupants + β2*buildingAge + β3*buildingSize + β4*buildingType +
β5*RoofInsulation + β6*solarInfluxi,t + εi,t
where the dependent variable Y is the willingness of a household i to use electrical energy for a specific
hour during day t. The αi reflects household-fixed effects that record potential time-invariant, householdlevel heterogeneity related to the willingness of a household to use electrical energy (Y). Moreover,
occupant is the number of people permanently occupying the household with the main effect β1. The
coefficient estimate of β1 indicates the main effect of the number of occupants on the household
willingness to use energy. Building age represents the age of a household’s building and β2 is the main
effect of the variable on the dependent variable. Additionally, the same effect is investigated for the size
of a household’s building, the type of the building, and the type of the roof insulation with the effects of
the variables on the dependent variable represented as β3, β4 and β5. Lastly, β6 reflects the main effect of
the control variable solar influx as a proxy for PV panel production for a household i at time t on the
household’s net electricity usage. Lastly, εi,t is an error term.
Moreover, the panel data regressions for the treatment group and the control group are compared and an
overall regression analysis including interaction terms for each independent variable with sample
membership (treatment/control) is used to determine whether the household attributes have a
significantly different relationship with the dependent variable in the dynamic pricing setting compared
to the TOU pricing setting.
Proving Statistical differences in the regression coefficients between treatment and control group
A dummy variable called ‘dynamic’ indicates whether a household was part of the treatment or the
control group. Additionally, five interaction variables were created in combination with the ‘dynamic’
variable. The interaction terms ‘Dynamic*Persons’, ‘Dynamic*BAge’, ‘Dynamic*BSize’,
‘Dynamic*BType’, and ‘Dynamic*RInsulation’ measure whether the influence of one of the predictor
variables is significantly different in the two groups, and to what amount the regression coefficient, and
hence the direction of the relationship, is different. Logically, the above-mentioned variables test the
hypothesis that
H0: βx treatment = βx control.
3.2.3 Research Question 1 – Analysis
Descriptive Statistics
Table five displays the descriptive statistics for the treatment group and the control group of this study.
A total of 47,275 observations can be made from the treatment group. Moreover, each of the 75
participating households has 720 observations made over one month. The average usage per price unit
28
Master Thesis – Navid Sadat-Razavi
is 2.115 kWh in the treatment group, while it is 3.524 kWh in the control group. This gives reason to
believe that the average price sensitivity of the treatment group can be considered higher than the average
price sensitivity of the control group.
Summary Statistics
usage/price - treatment group
N
Mean
Median
Min
Max
47,827
2.115
1
-34
37
3.524
2
-30
91.74
24,480
usage/price – control group
Table 5. Summary Statistics of Willingness To Use Electricity
Moreover, correlations between the variables are described in Table six. By observing the correlations
of the variables we are able to preliminarily test for cases of multicollinearity, hence, to test whether
certain predictor variables are influencing each other in a way that would bias the regression results. It
is worth pointing out that the variable House Type and Persons have a positive correlation of 0.26. This
can be attributed to the fact that certain household compositions, such as families, are likely to appear
in specific types of houses, then in, for instance, apartments. However, none of the predictor variables
showed correlations above the threshold of 0.5. We can thus infer that no signs of multicollinearity exist.
1
1. Usage / price
2
3
4
5
6
7
1
2. Persons
0,24
1
3. Building Age
-0,17
0,14
1
4. Building Size
0,2
0
-0,13
1
5. Building Type
0,13
0,26
-0,04
0,06
1
6. Roof Insulation
-0,11
0,15
0,01
0,04
0,08
1
7. Solar Influx
-0,29
0
0
0
-0,01
0
1
Table 6. Correlation Matrix
Results
A panel data regression analysis was performed in order to clarify the hypothesized relationships of H1,
H1a, H1b, H1c, H1d, H1e and H2. Before the initial analysis, the regression methods were subject to a
Hausman test, in order to determine the necessity to include fixed effects in the panel data regression or
not. For all 24 data sets, the p-value showed to be above the threshold of .05. Therefore, it is
recommended to use random effects for the panel data regression analysis (Green, 2008).
The Influence of Household Attributes on the Willingness of Households to Use Electricity
The first part of our analysis aims to assess how household attributes influence the willingness of
households to use electrical energy in a dynamic pricing setting. The following elaboration of this section
will clarify the influence of each household variable individually. Table seven displays the overall panel
data regression results over the day.
29
Master Thesis – Navid Sadat-Razavi
First, the number of occupants of a household has a constant significant positive relationship with a
household’s willingness to use electrical energy. The regression coefficient is 0.51 (0.14) and is
significant at the 99% level. This means that an increase of one household occupant results in an
increased willingness to use electrical energy of approximately 0.51kWh per €. Hence, it is reasonable
to claim that H1a is true and can be supported. The number of household occupants does have a
significant positive relationship with the willingness of that household to use electrical energy.
Second, the categorical variable building age has a significant positive relationship with a household’s
willingness to use electrical energy. Hence, the results indicate that the older a household’s building, the
higher the willingness of the household to use electrical energy. The regression coefficient is 0.25 (0.13)
and is significant at the 90% level. This means that the building age is significantly increasing a
household’s willingness to use electrical energy. Hence, we can regard H1b as true.
Third, the size of a household’s building has a significant positive relationship with a household’s
willingness to use electrical energy. The regression coefficient is 0.01 (0.002) and is significant at the
95% level. This means that the household willingness to use electricity increases by 0.01kWh per € per
one square meter increase of the building size in a given hour. Hence, we can regard H1c as true.
30
Master Thesis – Navid Sadat-Razavi
Panel Data Regression Results
Dependent variable:
Usage/Price
0.51***
Persons
(0.14)
0.25*
Building Age
(0.13)
0.01**
Building Size
(0.002)
Building Type
-0.05
(0.14)
Roof Insulation
-0.25
(0.44)
-0.01***
Solar Influx
(0.0003)
2.13**
Constant
(1.01)
Observations
47,827
R2
0.05
Adjusted R
F Statistic
Note:
2
0.05
436.36
*
***
(df = 6; 47820)
**
p<0.1; p<0.05; ***p<0.01
Table 7. Panel Data Regression Results RQ1
Hypotheses H1d and H1e cannot be supported according to our panel data regression results. However,
a more fine-grained analysis of 24 different panel data regressions has been conducted, in order to
examine during which hours of the day the household attributes are influencing the willingness to use
electricity.
The Difference of the Influence of Household Attributes Between Dynamic Pricing and TOU Pricing
Groups.
In an attempt to examine the differences of the previously investigated relationship, this study will
proceed to compare the average willingness to use electrical energy over the course of the day.
Subsequently, this study will compare the results of the 24 panel data regressions with the results of the
control group. Lastly, a test is conducted, in order to statistically prove that the influence of the
household attributes is significantly different between the two groups; control and treatment group.
Comparing the Average Willingness to Use Electrical Energy
It is reasonable to assume that dynamic electricity prices change a household’s willingness to use
electricity, as the constant reminder of the electricity price can increase the price sensitivity. Figure three
is visualizing the average willingness of the two samples to use electricity over the course of the day. It
can be generally stated that the willingness of those households exposed to dynamic prices, to use
electrical energy, is lower than the willingness of the control group. It is especially remarkable to see
31
Master Thesis – Navid Sadat-Razavi
that the willingness of the households of the treatment group is even decreasing between 10AM and
3PM, while it stays generally constant for the control group. Additionally, it is observable that the slope
is steeper when households start to increase the willingness to consume electrical energy between 5PM
and 6PM in the treatment group compared to the control group. Hence, from 5PM onwards, households
are slowly going back to their old willingness to use electrical energy, but never fully reach it until
11PM.
Willingnesstouseelectricity
(usage/price)
Willingnesstouseelectricityoveraday
6
5
4
3
TreatmentGroup
2
ControlGroup
1
0
1
3
5
7
9 11 13 15 17 19 21 23
Time(h)
Figure 3. Comparing the Willingness to Use Electricity between Treatment and Control Group
When comparing the regression results of the control group to the regression results of the treatment
group, it becomes evident that the influence of the household attributes on the willingness to consume
electrical energy is different. Figure four is visualizing the differences in the regression results between
both groups.
When examining the 24 different panel data regression coefficients between treatment and control group
in tables eight and nine in the Appendix, it becomes evident that the household attributes have a very
different influence on both groups. The observed differences are either time-differentiated or show an
opposite relationship with the willingness to use electrical energy. Figure four provides a rough overview
of the outcomes of the 24 panel data regressions. Additionally, we can see that some variables, such as
the building size, have a distinct gap in which the influence of the variable is not relevant for household
willingness to use electricity. Moreover, the variables building type and roof insulation showed to be
insignificant in the overall panel data regression, but have specific times of the day where they indeed
are significant.
32
Master Thesis – Navid Sadat-Razavi
Figure 4. Comparison of Regression Results between Treatment and Control Group
Thus, we conclude that there is an observable difference in the regression results between the treatment
and the control group. In order to statistically prove that this difference exists, and whether the strength
of the relationship between the household variables and household willingness to use energy is weaker
in a dynamic pricing setting compared to a TOU pricing setting, an additional statistical test was
conducted.
Proving Statistical Differences in the Regression Coefficients between Treatment and Control Group
When interpreting the results in table eight, it is vital to recall that the variable dynamic takes the value
1 for households of the treatment group and the value 0 for households of the control group.
Consequently, the households of the control group are the omitted group for the results.
The variable ‘Dynamic*Persons’ corresponds to the difference of the slopes for the variable Persons
between the treatment and the control group (slope treatment group – slope control group). The
regression coefficient is 0.33 (0.03) and is significant at the 99% level. Hence, the number of occupants
is increasing household willingness to use energy by 0.33kWh per € more in the treatment group than
in the control group. This is a surprising finding, as this study has assumed that the influence of
household attributes will be weaker in a dynamic pricing setting.
Moreover, the regression coefficient of ‘Dynamic*BAge’ is -0.22 (0.03) and is significant at the 99%
level. Hence, the building age is having a significantly different influence on household willingness to
use energy in the treatment group than in the control group. More specific, the slope is -0.22 weaker in
the treatment group than in the control group.
The regression coefficient of ‘Dynamic*BSize’ is -0.02 (0.0004) significant at the 99% level. Hence,
the size of a household’s building is positively influencing a household’s willingness to use energy in a
dynamic pricing environment, but to a lower extent than in a TOU pricing environment.
Furthermore, the regression coefficient of ‘Dynamic*BType’ is -0.29 (0.02) and significant at the 99%
level. Consequently, it is safe to say that the building type of a household is influencing household
willingness to use energy at a significantly lower degree in the treatment group than in the control group.
33
Master Thesis – Navid Sadat-Razavi
Lastly, the influence of the variable Roof Insulation has not been found to be significantly different
between the treatment and the control group.
Consequently, this study concludes that Hypothesis 2 can be partly supported by our findings. The
relationship between the attributes of a household and the willingness to use energy is weaker in a
dynamic pricing setting compared to a TOU pricing setting with one exception. The number of
household occupants has a significantly higher influence on the dependent variable in the dynamic
pricing setting. Explanations why this variable in specific has shown a different relationship can be
found in the Discussion section of this study.
Coefficient Test Results
Willingness to use electrical energy
(Usage/Price)
Y
Dynamic
-0.40**
(0.19)
Persons
0.18***
Building Age
(0.02)
0.06***
Building Size
(0.02)
0.02***
(0.0003)
Building Type
0.20***
(0.02)
Roof Insulation
-0.47
(0.06)
Dynamic*Persons
0.33***
Dynamic*BAge
(0.03)
-0.22***
(0.03)
Dynamic*BSize
-0.02***
(0.0004)
Dynamic*BType
-0.25***
(0.02)
Dynamic*RInsulation
Solar Influx
0.29
(0.08)
-0.01***
(0.0002)
Constant
2.08***
(0.25)
Observations
72,273
N
R2
Adjusted R2
108
0.13
0.13
F Statistic
803.61*** (df = 12; 72259)
*
Note:
p<0.1; **p<0.05; ***p<0.01
Table 8. Testing Influence of Household Attributes between Treatment and Control Group
34
Master Thesis – Navid Sadat-Razavi
Robustness of the Models and Explanatory Power
The adjusted R2 of the 24 panel data regression models ranged between 2% and 15%, meaning that the
model used for the regression analysis only explained between 2% and 15% of the changes in the
households’ willingness to use energy. Moreover, the explanatory power of our model is, compared to
the other times of the day, higher during the evening times. Although such low levels of explanatory
power indicate a weakness of the model used in this study, it can be argued that comparable studies have
found similar levels of accuracy. Filippini (2011) found R2 values between 25.2% and .6%, depending
on the investigation of peak and off-peak energy consumption. Hence, we can consider the explanatory
power of our model as weak but acceptable. Another possible explanation for the low explanatory power
is the fact that the dependent variable household net electricity usage depends on several factors, such
as sunshine hours and PV panel production, and is therefore harder to determine than pure electricity
consumption.
The Influence of the Control Variable Solar Influx on the Willingness to Use Electricity
The control variable solar influx could only be implemented in the models for 8AM until 8PM. The
reason for this is that the variable Solar Influx was constant of value 0 during times where the sun was
not shining and could therefore only be included during the times where the sun was shining.
Furthermore, Solar Influx in J/cm3 is significantly influencing net electricity usage between 8AM and
8PM. Since net electricity usage is computed as electricity consumption subtracted by electricity
production, it is self-explanatory why this relationship exists.
3.3 Analysis of Relative Electricity Usage (RQ2)
3.3.1 Method
In order to investigate RQ2, another panel data regression has been conducted. Further details about the
panel data regression analysis can be read in section 3.2.
Data Preparation
In order to answer the second research question, the collected hourly consumption data were analyzed
in several steps. First, two separate panel data regressions were conducted in order to examine the
different results between the dynamic pricing and the TOU pricing group. Subsequently, a test
determines whether dynamic electricity prices and TOU electricity prices have a significantly different
influence on the relative electricity usage of households. Lastly, the data set is a split into 24 different
data sets, one data set for each hour of the day. By investigating the relative daily electricity usage during
each hour of the day, this study aims to investigate during what hours of the day households are changing
their behavioral usage patterns due to varying price levels.
Equation 2
The electricity usage behavior is represented by the relative electricity usage at hour t, as a share of the
daily total electricity usage.
Relative daily electricity usage = Usagei,t / Daily sum of usagei
35
Master Thesis – Navid Sadat-Razavi
We have aimed to measure the effect of prices on the electricity usage behavior of households. The
electricity usage behavior is computed as the relative electricity usage of a specific day, in percent.
Hence, the total sum of the relative daily electricity usage should always add up to 1. This variable will
enable us to understand if households are able to change their consumption patterns and participate in
load shifting behavior.
Investigating Differences in the Influence of Dynamic and TOU Prices on Behavioral Usage Patterns
In a first step, we are going to compare the differences in the regression models of the dynamic pricing
and the TOU pricing group. Subsequently, we will conduct an overall regression analysis including an
interaction term of prices and treatment group membership, in order to determine whether the influence
of the two different types of prices is statistically significant. More specific, a regression analysis
including a dummy variable called ‘dynamic’ indicates whether a household was part of the treatment
or the control group. Additionally, an interaction variable was included in combination with the
‘dynamic’ variable. The interaction term ‘Dynamic*Price’ measures whether the influence of the
predictor variable is significantly different in the two groups and to what amount the regression
coefficient, and hence the direction of the relationship is different. Logically, the above-mentioned
variables test the hypothesis that
H0: βx treatment = βx control.
Investigating Changes in Behavioral Patterns during each Hour of the Day
Subsequently, we will proceed to investigate the relationship between dynamic electricity prices and the
relative electricity usage of households during each hour of the day. By looking at each hour
independently, we hope to identify specific times during the day, in which the households of our sample
are adapting their behavior more rigorously in order to take advantage of varying price levels.
Additionally, if the relative daily share during a specific hour decreases with increasing dynamic prices,
it is reasonable to assume that a load shift has occurred, as the absolute consumption at the moment in
time must have decreased and the absolute consumption during other times of the day must have
increased. In the setting of our study, this means we will investigate whether the dynamic electricity
prices of ‘Qurrent Energie’ enable the participating households to shift part of their daily consumption
to a different time with low electric charges.
The Hypothesis three is covered by this equation. We derive the following equation for the abovementioned analytical approach.
Equation 2: Investigating the Influence of Dynamic Prices on the Daily Share of Electricity Usage of
Households
YUsage%i,t = αi + β1*pricet + β2*SolarInfluxi,t + εi,t
where the dependent variable Y is the relative daily electricity usage of a household i during a specific
moment of the day t. The αi reflects household-fixed effects that record potential time-invariant,
household-level heterogeneity related to the net electricity usage (Y). Moreover, pricet is the price
displayed to the households during the time t, with the main effect β1. The coefficient estimate of β1
36
Master Thesis – Navid Sadat-Razavi
indicates the main effect of the price on the relative daily electricity usage of a household i. Moreover,
the control variables AvSolarInflux is included with the main effect β2. Lastly, εi,t is an error term.
3.3.2 Research Question 2 – Analysis
Descriptive Statistics
Figure five and six display the dynamic and the TOU pricing scheme charged to the households during
the month of March of the pilot project. The TOU pricing scheme consisted of a peak price (08:00 –
20:00) of €0.1868/kWh and an off-peak price (21:00 – 07:00) of €0.1744/kWh. The dynamic pricing
scheme was coupled to the APX day-ahead market prices. Naturally, the dynamic pricing scheme of this
study is more complex than the TOU pricing scheme. Figure five and table 11 in the Appendix provide
a better overview of different prices charged to the treatment group. The highest recorded price in March
was charged at 7PM. Moreover, the highest average price was charged at 8PM during the course of the
experiment. Lastly, the lowest recorded price was charged during 9AM, while the lowest average price
was charged at 5AM.
AverageDynamicPricewithMinandMax
0,2
0,18
0,17
DynamicPrice
0,16
Series2
Min
0,15
0,14
Max
Series3
00:00:00
01:00:00
02:00:00
03:00:00
04:00:00
05:00:00
06:00:00
07:00:00
08:00:00
09:00:00
10:00:00
11:00:00
12:00:00
13:00:00
14:00:00
15:00:00
16:00:00
17:00:00
18:00:00
19:00:00
20:00:00
21:00:00
22:00:00
23:00:00
Price(€)
0,19
Time(h)
Figure 5. Dynamic Pricing Scheme with Min and Max.
37
Master Thesis – Navid Sadat-Razavi
Price(€)
TOUPricingScheme
0,183
0,178
TOU
Series1
Price
00:00:00
01:00:00
02:00:00
03:00:00
04:00:00
05:00:00
06:00:00
07:00:00
08:00:00
09:00:00
10:00:00
11:00:00
12:00:00
13:00:00
14:00:00
15:00:00
16:00:00
17:00:00
18:00:00
19:00:00
20:00:00
21:00:00
22:00:00
23:00:00
0,173
Time(h)
Figure 6. TOU Pricing Scheme.
Results
The Influence of Dynamic Electricity Prices on the Relative Electricity Usage
In order to understand whether dynamic electricity prices are influencing the electricity usage behavior
of the participating households of the pilot project, it is essential to examine the relative usage profile.
Figure seven visualizes the relative load profiles of the treatment and the control group. Looking at
Figure seven, it becomes evident that the electricity usage behavior of households is different in a
dynamic pricing setting compared to a TOU pricing setting. More specific, when comparing the two
graphs, it is observable that part of the relative electricity usage during the afternoon has been shifted to
the late evening hours. Moreover, another, smaller shift occurs between 10AM and 2PM in which
households use less electricity in the morning in order to consume it immediately after noon.
AverageRelativeLoadProfile
TreatmentGroup
ControlGroup
relativeelectricityusage
0,08
0,07
0,06
0,05
0,04
0,03
0,02
0,01
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time(h)
Figure 7. The Average Relative Load Profiles of Treatment and Control Group.
38
Master Thesis – Navid Sadat-Razavi
This finding gives reason to believe that dynamic electricity prices have a different relationship to the
relative electricity usage of households than TOU prices. The following elaborations are aimed to clarify
whether the influence of TOU prices is significantly different from the influence of dynamic prices on
the relative electricity usage of households.
The Difference of the Influence of Electricity Prices Between Dynamic Pricing and TOU Pricing
Groups.
In an attempt to examine the differences of the previously investigated relationship, we will proceed to
compare the overall panel data regression models of the treatment and the control group. Subsequently,
we are going to statistically prove that the influence of the dynamic prices is significantly different from
the influence of TOU electricity prices.
Table nine shows the overall panel data regressions comparison between the treatment and the control
group. The results show that the dynamic electricity prices have a positive significant relationship with
a regression coefficient of 0.78 (0.28), while the TOU electricity prices have a positive significant
relationship with a regression coefficient of 0.95 (0.04). The relationships are significant at the 95% and
99% level respectively. Consequently, we can see that the dynamic electricity prices have a less positive
relationship with the relative electricity usage of households compared to the TOU electricity prices.
Panel Data Regression Results
Relative Electricity Usage
Treatment
Dynamic Price
0.78
Control
***
(0.28)
0.95***
TOU Price
(0.04)
SolarInflux
-0.0002
***
(0.0000)
Constant
-0.10
Observations
N
R2
Adjusted R
Note:
2
**
-0.0000***
(0.0000)
-0.13***
(0.05)
(0.01)
47,275
103,515
73
150
0.34
0.48
0.34
*
**
p<0.1; p<0.05;
0.48
***
p<0.01
Table 9. Comparison of Regression Results
39
Master Thesis – Navid Sadat-Razavi
Proving Statistical Differences in the Regression Coefficients Between Treatment and Control Group
When interpreting the results in table ten, it is vital to recall that the variable dynamic takes the value 1
for households of the treatment group and the value 0 for households of the control group. Consequently,
the households of the control group are the omitted group for the results.
Moreover, the variable ‘Dynamic*Price’ corresponds to the difference of the slopes for the variable
Price between the treatment and the control group (slope treatment group – slope control group). The
regression coefficient is -0.16 (0.31) and is significant at the 95% level. Hence, the dynamic electricity
price has a significantly lower positive influence in the relative electricity usage of households than the
TOU electricity price.
Therefore, we can confirm that the influence of dynamic electricity prices throughout the entire day is
significantly less positive than the influence of the TOU prices. Hence, Hypothesis 3 can be supported;
dynamic electricity prices have a significantly different influence on the relative electricity usage of
households compared to TOU electricity prices.
Coefficient Test Results
Relative Electricity Usage
0.03**
Dynamic
(0.05)
0.95***
Price
(0.27)
-0.16**
Dynamic*Price
(0.31)
Solar Influx
-0.00
(0.0000)
-0.13***
Constant
(0.05)
Observations
155,676
N
R2
220
0.02
Adjusted R2
F Statistic
0.02
45.92
*
***
(df = 4; 155452)
p<0.1; **p<0.05; ***p<0.01
Table 10. Statistical Significance of Differences in Regression Coefficients between Treatment and Control Group
40
Master Thesis – Navid Sadat-Razavi
Investigating the Influence of Dynamic Prices during Every Hour of the Day
In order to statistically prove that the load shifts can be attributed to the dynamic prices, we will proceed
to perform 24 different panel data regressions, one for each hour of the day. By examining the influence
of the prices on the relative electricity usage, this study aims to clarify changes in behavioral patterns of
the households.
Additionally, the panel data regression methods were subject to a Hausman test, in order to determine
the necessity to include fixed effects in the panel data regression or not. The p-value showed to be above
the threshold of .05. Therefore, it is safe to use random effects for the panel data regression analyses.
The results of the panel data regressions can be found in table eleven.
Looking at the results, it becomes evident that the dynamic electricity prices have a significant negative
relationship with the relative electricity usage of households between 8AM and 5PM. The strongest
relationship occurs at 1PM, with a regression coefficient of -0.08 (0.02) and is significant at the 99%
level. Hence, this study can claim that the households of the ‘Qurrent Energie’ pilot project successfully
manage to increase their relative electricity usage when the dynamic electricity price is low and decrease
their relative electricity consumption when the dynamic electricity price is high, between 8AM and 5PM.
Consequently, the findings of this study cannot support Hypothesis 3a. The dynamic electricity prices
between 8AM and 5PM do show a significant relationship with the relative electricity usage, a negative
one.
Moreover, the findings of this study cannot support Hypothesis 3b as well. The dynamic electricity
prices between 5PM and 7PM only show a significant negative relationship with the relative electricity
usage of households at 5PM but not at 6PM or 7PM.
Lastly, the deviations in the relative electricity usage visible in Figure seven occur exactly during the
times in which the panel data regressions of our study prove that households are capable of appropriately
adapting their behavior to increasing and decreasing electricity prices. Hence, there is sufficient evidence
to claim that the households of the ‘Qurrent Energie’ pilot are shifting part of their electricity usage to
different times of the day. As our results have shown, it is likely that these shifts are encouraged by the
dynamic prices. Hence, Hypothesis 3c can be supported. The dynamic electricity prices encourage the
households to shift loads.
41
Master Thesis – Navid Sadat-Razavi
Panel Data Regression Results
Dependent variable: Relative Electricity Usage
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
-0.03***
(0.01)
price1
price2
0.01
(0.01)
price3
-0.005
(0.01)
-0.01**
(0.01)
price4
price5
-0.01
(0.01)
0.02***
price6
(0.005)
price7
-0.0003
(0.01)
-0.01*
price8
(0.004)
-0.02***
price9
(0.01)
-0.05***
(0.01)
price10
-0.06***
(0.01)
price11
-0.05***
(0.02)
price12
-0.08***
(0.02)
price13
-0.07***
(0.01)
price14
-0.07***
(0.01)
price15
-0.03***
(0.01)
price16
-0.02*
(0.01)
price17
price18
-0.01
(0.01)
price19
0.01
(0.01)
price20
-0.01
(0.01)
price21
0.003
(0.01)
-0.03**
(0.01)
price22
price23
0.17
(0.22)
-0.97**
price24
(0.39)
SolarInflux
Constant
Observations
R2
Adjusted R2
F Statistic
-0.000
-0.00***
-0.00***
-0.00***
-0.00***
-0.00***
-0.00***
-0.00***
-0.00***
-0.00***
-0.01***
0.001**
0.04***
(0.002)
0.04***
(0.002)
0.04***
(0.002)
0.04***
(0.001)
0.04***
(0.001)
0.04***
(0.001)
0.04***
(0.001)
(0.000)
0.04***
(0.001)
(0.000)
0.05***
(0.002)
(0.000)
0.06***
(0.002)
(0.000)
0.06***
(0.003)
(0.000)
0.06***
(0.003)
(0.000)
0.06***
(0.003)
(0.000)
0.06***
(0.002)
(0.000)
0.06***
(0.003)
(0.000)
0.05***
(0.002)
(0.000)
0.05***
(0.002)
(0.000)
0.04***
(0.002)
(0.004)
0.04***
(0.002)
0.04***
(0.002)
0.04***
(0.002)
0.04***
(0.002)
0.01
(0.4)
0.43***
(0.10)
1,990
0.05
0.05
1,923
0.06
0.06
1,987
0.07
0.07
1,991
0.09
0.09
1,991
0.09
0.09
1,991
0.04
0.04
1,991
0.03
0.03
1,991
0.04
0.04
1,991
0.07
0.07
1,991
0.10
0.10
1,991
0.11
0.11
1,991
0.12
0.12
1,991
0.11
0.11
1,991
0.12
0.12
1,991
0.09
0.09
1,991
0.05
0.05
1,991
0.03
0.03
1,991
0.06
0.06
1,991
0.04
0.04
1,991
0.03
0.03
1,991
0.03
0.03
1,991
0.03
0.03
1,999
0.01
0.01
2,100
0.01
0.01
25.75***(df
31.98***(df
36.54***(df
50.71***(df
50.54***(df
22.46***(df
17.70***(df
15.81***(df
28.56***(df
= 5; 1985)
42.55***(df
= 5; 1985)
50.98***(df
= 5; 1985)
52.26***(df
= 5; 1985)
49.79***(df
= 5; 1985)
53.20***(df
= 5; 1985)
38.66***(df
= 5; 1985)
20.77***(df
= 5; 1985)
13.66***(df
= 5; 1985)
24.25***(df
= 5; 1985)
15.13***(df
= 5; 1985)
17.83***(df
= 4; 1986)
14.54***(df
= 4; 1986)
13.58***(df
= 4; 1986)
-43.86
(df = 4;
1994)
2.90**(df
= 4;
2095)
= 4;11.
1985)24h
= 4;Panel
1918) Data
= 4; 1982)
= 4; 1986) Results
= 4; 1986) of= Dynamic
4; 1986)
= 4;Prices
1986)
= 5; 1985)
Table
Regression
42
Master Thesis – Navid Sadat-Razavi
Robustness of the Models and Explanatory Power
The adjusted R2 of the 24 panel data regression models has ranged between 1% and 12%, meaning that
the model used for the regression analysis has only explained between 1% and 12% of the changes in
the households’ willingness to use energy.
The Influence of the Control Variable Solar Influx on the Relative Electricity Usage
The solar influx does show a significant negative relationship with the dependent variable. Therefore, it
is safe to assume that the solar power production of a household, and the weather conditions on a day
significantly influence a household’s relative usage patterns throughout the day, in the ‘Qurrent Energie’
pilot project.
3.4 Analysis of Household Segmentation (RQ3)
3.4.1 Method
Principal Component Analysis
A Principal Component Analysis (PCA) analyzes a data set, with the goal to extract important
information from the data and to express this information as a set of new orthogonal variables called
principal components (Abdi & Williams, 2010). A PCA has three general goals. First, a principal
component analysis aims to extract the most important information from the data set. Second, a PCA
aims to compress the size of the data set by keeping only this important information. Third, a PCA aims
to simplify the description of the data set and to analyze the structure of the observations and variables
(Abdi & Williams, 2010). Subsequently, PCA computes new orthogonal variables out of the variables
of the data set called principal components. The first principal component is supposed to have the largest
possible variance and thus explains the largest part of the inertia of the data set. The second and all
following principal components are computed under the constraint of being orthogonal to the previous
component, in order to have the largest possible inertia.
This study will conduct a principal component analysis (PCA) and plot the first two principal axes of
the PCA with the outlined clusters. If the outlined clusters are strong at the selected levels, the clustering
plot should not display substantial overlaps.
Cluster Analysis
Following the investigations on household attributes and dynamic prices for electricity during different
times of the day, this study aims to segment the participating households into objectively distinguishable
household archetypes. The underlying goal of this segmentation strategy is to prove the assumption that
different groups of energy consumers have varying capabilities to react to dynamic electricity prices.
A clustering analysis is a technique that is used to segment elements in data sets in a way that similar
elements are assigned to the same cluster, while elements with different attributes are assigned to a
different cluster. In this way, clustering is particularly useful to efficiently search for groups of elements
in a multi-dimensional data set (Bhatia, 2004). By following an unsupervised clustering approach, we
43
Master Thesis – Navid Sadat-Razavi
aim to identify groups of households that are different in their household composition and their buildings
conditions.
Many different types of clustering methods are discussed in the academic literature and can be generally
categorized into hierarchical and non-hierarchical clustering methods. Hierarchical clustering methods
aim to cluster a data set in a bottom-up approach, starting with the same number of clusters as instances
available in the data set and gradually grouping similar instances together. Non-hierarchical clustering
methods aim to determine the number of clusters prior to clustering and assign instances to the cluster
centroids that are closest to them. We will use a k-means clustering approach in order to identify clearly
distinguishable household segments.
K-means clustering
The k-means clustering technique is one of the earliest clustering techniques in the literature (Anderberg,
1973). In k-means clustering, clustering is based on the identification of K elements in the data set that
are used to establish an initial representation of clusters. Moreover, these K elements form the cluster
seeds, to which other elements are then assigned to form clusters (Bhatia, 2004). The ultimate goal of
the k-means algorithm is to assign each element of a data set to a cluster, with the aim to maximize the
between-cluster variation while minimizing the within-cluster variation.
Choosing the Correct Cluster Solution
This study will follow a k-means clustering analysis, in order to come up with meaningful household
groups. In a first step, a set of tests will be conducted with the aim to identify the correct cluster solution
for the data set of this study. More specific, we will proceed to compare the sum of squared error (SSE)
for a number of cluster solutions. SSE is defined as the sum of the squared distance between a cluster
element and the centroid of its cluster (Peeples, 2011). Hence, the SSE can be regarded as a global
measure of error that decreases with an increasing number of clusters. Additionally, we will compare
the SSE over cluster solutions with the SSE of 250 randomized versions of the original input data, in
order to understand if the original data set has an underlying structure that allows it to be appropriately
clustered. Lastly, we will examine the absolute differences between the actual and the random SSE
against the tested cluster solutions. Ideally, the cluster solution with the highest difference between the
SSE of the original data set and the randomized dataset should be chosen for the k-means analysis.
Subsequently, we will perform the aforementioned k-means clustering analysis. The k-means approach
aims to assign each household to the cluster with the closest cluster center. The k-means analysis was
run with a variety of different cluster sizes, ranging from 2 clusters up to 15 clusters. The Hypothesis
four is addressed through the proposed clustering analysis. The results can be seen in the following
section.
3.4.2 Research Question 3 - Analysis
Before our initial analysis, a PCA was conducted with the aim to identify the most powerful variables
for the cluster analysis of our households. The PCA results can be seen in Table 16 in the Appendix. We
have proceeded to select the four strongest variables of the first two components, as these are already
capable of explaining 50% of the point variability of the entire data set.
44
Master Thesis – Navid Sadat-Razavi
The four variables used for the cluster analysis were as follows:
Persons – The number of people permanently occupying a household.
Building Size – The size of a household’s building measured in square meters.
Building Type – One of five different types of buildings a household is living in.
Terrain Type – A categorization indicating whether the building of a household is located in a rural,
suburban or urban area.
Choosing the Correct Cluster Solution
In a first step, we have compared the within group SSE and the log of the within group SSE between the
actual data set and 250 different randomized versions of the data set. In order to successfully prove that
the original data set possesses an underlying structure, the within group SSE and the log of the within
group SSE need to be decreasing faster than the within group SSE of the respective randomized samples.
An overview of this test can be found in figure eight and figure nine.
Figure 8. and 9. Within Group SSE of Actual and 250 Randomized Data Sets against 15 Cluster Solutions
Looking at the two figures above, it becomes evident that the within group SSE and the log of the within
group SSE is lower in the actual data set. Consequently, we can claim that the chosen data set possesses
an underlying structure and clusters are present. However, it is hard to determine the ‘elbow’ in the scree
plot, which normally indicates the ideal cluster solution for this data set.
Another way to evaluate the appropriate cluster solution is to examine the absolute differences between
the original and random SSE against the chosen cluster solutions (Peeples, 2011). The appropriate
cluster solution will be the solution at which the actual SSE differs the most from the mean of the random
SSE. In order to visualize this comparison, figure ten and figure eleven have been developed.
45
Master Thesis – Navid Sadat-Razavi
Figure 10. and 11. The difference of Within Group SSE of Actual and 250 randomized Data sets against 15 Cluster Solutions.
Inspecting both figures, it becomes evident that the highest recorded differences between the actual and
the random SSE is in both cases the five cluster solution. Therefore, we will follow a five cluster solution
for the remainder of the analysis.
Principal Component Analysis
Subsequently, a principal component analysis has been conducted, in order to better visualize the cluster
solutions along the two strongest principal components. The results of the PCA can be seen in Table 12.
PC1
PC2
PC3
PC4
Standard deviation
1,4419
1,0237
0,9343
0,0000
Proportion of Variance
0,5198
0,2620
0,2182
0,0000
Cumulative Proportion
0,5198
0,7817
1,0000
1,0000
Persons
0,4427
-0,4998
0,6156
0,4187
Building Size
-0,6935
-0,0138
-0,0024
0,7204
Building Type
0,5546
0,1914
-0,6076
0,5355
Terrain Type
Table 12. PCA Results
0,1250
0,8446
0,5019
0,1381
Consequently, the five cluster solution is visualized in Figure 12.
46
Master Thesis – Navid Sadat-Razavi
Figure 12. Visualization of Five-Cluster Solution of K-Means Analysis Along the Two Strongest Principal Components
As visible from figure 12, the data set was segmented into clearly distinguishable clusters. We can thus
confirm that the chosen clustering solution is strong. The two strongest principal components of the
PCA explain 78.17% of the point variability within our cluster.
The following elaborations are going to deepen the analysis of the identified clusters. More specific, we
are going to explain and define the different clusters. Lastly, we are going to examine whether the cluster
analysis based on household attributes was able to segment groups that are also distinguishing
themselves in the way they are capable of reacting to dynamic electricity prices.
47
Master Thesis – Navid Sadat-Razavi
Interpreting and Profiling the Clusters
Each of the five identified clusters of this study should be seen as an archetype that categorizes the
households across the sample of the ‘Qurrent Energie’ pilot program. The following section is going to
provide a general overview of the attributes of each cluster and will compare how these attributes
compare between the archetypes. An overview of the distribution of the 75 clustered households of this
study across the identified clusters is provided in Figure 13.
Since Cluster 1 is only comprised of 2 households, this cluster will be ignored for the following
elaborations.
Furthermore, ‘Cluster 2’ is characterized
by a small group composition of 2 persons
on average, and a wide variety of different
house types, that also differentiate
themselves substantially in their size.
Additionally, this group primarily contains
urban households.
‘Cluster 3’ represents 21% of the
participating households. Similar to
‘Cluster 2’, this cluster primarily contains
two-person households, which either live in
detached houses or corner houses, that are
approximately 150m2 big. Moreover, this
cluster is comprised of a mix of urban, rural
and suburban households.
ClusterOverview
Cluster1
3%
Cluster2
23%
Cluster5
36%
Cluster4
17%
Cluster3
21%
‘Cluster 4’ represents 17% of the
participating households. Other than the
Figure 13. Overview of the Distribution of Clusters
previous clusters, this group of households
consists of four-person groups on average, that are only residing in urban or suburban areas. Moreover,
the majority of this group resides in semi-detached and row-houses.
Lastly, ‘Cluster 5’ represents the largest group of households with 36%. This group represents
households of two to four persons that primarily live in row houses, detached houses, and semi-detached
houses. The terrain type is distributed across all three types.
Household Occupancy
The average household occupancy across all clusters was 2.63 people per household. As visible from
Figure 14. The biggest household groups can be found in Cluster 4, with 4.15 occupants on average.
Disregarding Cluster 1, the smallest group of households can be found in Cluster 2 with exactly 2
occupants on average.
48
Master Thesis – Navid Sadat-Razavi
NumberofOccupants
5
4
3
2
1
0
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Figure 14. Average Household Occupancy for Each Cluster
Building Type
The distribution of building types across the five identified clusters can be found in Figure 15. As visible,
the biggest proportion of detached and corner houses can be found in Cluster three. This is an interesting
finding, as the cluster with the highest amount of household occupants does not possess the largest type
of building. Moreover, it can be observed that the majority of semi-detached houses can be found in
Cluster four, while the Building Type composition in Cluster two and three are mixed.
DistributionofBuildingTypepercluster
1
0,8
Semi-detachedHouse
0,6
Apartment
0,4
RowHouse
0,2
DetachedHouse
CornerHouse
0
1
2
3
4
5
Figure 15. The Distribution of Building Types for Each Cluster
Building Size
The average building size across all clusters can be observed in Figure 16. The average building size
across all clusters was 158.2m2 per household. The biggest houses are owned by Cluster 2 with an
average building size of about 200m2. Moreover, the smallest average building size can be found in
Cluster 4, with an average size of about 130m2. This is a surprising finding, since Cluster 2 has an
average household occupancy of 2 persons, while Cluster 4 has an average household occupancy of 4.15
persons.
49
Master Thesis – Navid Sadat-Razavi
BuildingSize
250
200
150
100
50
0
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Figure 16. Average Building Size for Each Cluster
Terrain Type
The distribution of terrain type across the five identified clusters can be found in Figure 17. It is worth
noting that Cluster 2, despite having the biggest building sizes, has the highest proportion of urban
households. Especially when taking into consideration that Cluster 2 has an average household
occupancy rate of 2 persons, it becomes evident that the income per person of this household might be
unproportionally higher than in the rest of the sample. Furthermore, it is worth noting that Cluster 3 has
the highest proportion of households based in rural areas, which might explain why this Cluster also has
the highest proportion of detached houses. Lastly, Cluster 4 has the highest share of households based
in Suburban areas.
DistributionofTerrainTypeperCluster
1
0,8
0,6
Rural%
0,4
Suburban%
0,2
Urban%
0
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Figure 17. Distribution of Terrain Type per Cluster
Assessing Differences in Behavioral Patterns of the Identified Clusters during the Qurrent Project
The relative electricity usage across clusters can be seen in Figure 18. Looking at the graph, it is
recognizable that Cluster 3 is showing extreme behavioral deviations in their relative electricity usage.
Comparing it to the relative electricity usage of our control group, it becomes evident that Cluster 3 is
exchanging part of its daily energy consumption to early noon hours, and saving high amounts during
afternoon hours, when the electricity prices are starting to rise again. In addition, Cluster 2 is showing a
similarly favorable relative load profile for the dynamic prices. More specific, Cluster 2 has an above
50
Master Thesis – Navid Sadat-Razavi
average relative electricity usage during early morning and late evening times, which are usually the
times in which the prices are comparably low. Lastly, Cluster 4 and 5 have very similar relative load
profiles compared to the control group. We can thus assume that the dynamic electricity prices had little
influence on the behavioral patterns of these households.
Figure 18. Differences in the Relative Electricity Usage Across the Identified Clusters
51
Master Thesis – Navid Sadat-Razavi
4. Discussion
4.1 The Influence of Household Attributes on Willingness to Use Electricity
We have investigated the overall influence of our household attributes on the willingness of households
to use electricity, in order to understand what aspects of a household explain variations on electricity
usage. Our results have shown that the number of household occupants, the building age, and the
building size positively influence the household willingness to use electricity. Hence, the more people
living in a household, the higher the willingness to use electricity; the older a building, the higher the
willingness to use electricity; and the bigger a building, the higher the willingness to use electricity.
However, previous studies have found the influence of building age to be negatively related with
electricity usage, due to the presence of more electrical appliances in modern houses (Statistik Austria,
2011). One very obvious explanation for these results is that the absolute electricity usage is simply
higher in these type of households (McLoughlin et al., 2012; Yohannis et al., 2008). However, it can
also be argued that individuals living in households with these attributes perceive their individual
impact as irrelevant, due to fixed factors such as a building’s energy inefficiency due to its age, or the
unfavorable behavior of other household occupants (Ingham et al., 1974). As a result, households with
high values for these variables can be considered less likely to respond to dynamic electricity prices.
For the same reasons, we can assume that the households are less responsive to the delivery of new
electricity-related information and base their consumption patterns on habits rather than rational
decisions (Fischer, 2008). Therefore, a more fine-grained delivery of electricity usage information
should counter this effect and decrease the strengths of the relationship between household attributes
and electricity usage (Matthies, 2005). One possible way to increase the level of delivered information
is the introduction of hourly changing prices (dynamic prices) for TOU prices, as dynamic price signals
constantly remind households about their electricity consumption patterns and reflect the current
electricity market situation.
Therefore, we have examined whether the influence of household attributes on the willingness to use
electricity is significantly different in a dynamic pricing environment compared to a TOU pricing
environment. According to our findings, this is indeed the case, as all household characteristics other
than the availability of roof insulation have a significantly different influence on the willingness to use
electricity. More specific, the variables building age and building size have a weaker positive
relationship with willingness to use electricity in the dynamic pricing setting than in the TOU pricing
setting. This supports our earlier argument that a more fine-grained delivery of electricity information
decreases the strength of the relationship between household attributes and willingness to use
electricity, as we can regard the dynamic electricity price as an enhanced way of communicating the
electricity market situation to residential households. Based on these findings, we can also make the
assumption that the investigated household attributes reflect lifestyle patterns of households (Hayn
et al., 2014). With the relationship of the household attributes on willingness to use electricity
decreasing, it is possible that these lifestyle patterns have changed or have become less relevant
predictors of electricity usage. However, one exception contrasting this assumption are the number of
household occupants, whose influence on willingness to use electricity has actually increased. A
possible reason for this could be the excitement of individuals to be part of the dynamic pricing sample,
and thereby consume more electricity than usual, as described by the Hawthorne-effect (McCarney et
al., 2007).
52
Master Thesis – Navid Sadat-Razavi
Lastly, we have conducted an hourly analysis to investigate the effect of household attributes during
different hours of the day. Our results show that the influence of household attributes is not constantly
significant throughout the day. Hence, consumer targeting initiatives in dynamic pricing settings
should follow a dynamic segmentation model that is capable of taking into consideration a
different set of variables for different times of the day. Another remarkable result is the influence of
the building size on willingness to use electricity. According to our results, building size positively
influences the willingness to use electricity from 1AM to 7AM and from 4PM to 12PM. Hence, the size
of a household’s building has not sufficiently explained variations in the price sensitivity of residential
households from 8AM to 3PM. The influence of a household’s building size can therefore be taken into
consideration in the morning and evening times when determining household willingness to use
electricity, but not during day times. Based on our previous assumption that our household attributes
reflect lifestyle patterns, we can assume that the building size between 8AM and 3PM is not sufficiently
explaining variations in the willingness to use electricity, because the households of our treatment
group have started to engage in more rational electricity usage behavior, based on prices rather
than habits.
In order to gain a better understanding of the dynamic electricity prices, another analysis has been
conducted, examining the influence of electricity prices on electricity usage behavior.
4.2 The Influence of Dynamic Prices on Usage Behavior
The two main goals for the analysis of the second section were to understand whether dynamic prices
and TOU prices influence consumption behavior in a significantly different way and during which times
of the day consumers are actually capable to change their behavioral consumption patterns as a reaction
to electricity prices.
Our findings have shown that the relative load profiles of the treatment and control group are different
from each other. More specific, the share of electricity usage is lower and higher at a few specific points
of the day in the treatment group. This gives reason to believe that the households exposed to dynamic
prices are shifting their loads by changing their behavioral patterns as a response to the electricity
prices. Additionally, we have found that the influence of the dynamic electricity prices is significantly
different from the influence of the TOU prices on the relative electricity usage of the households of our
study. More precisely, the influence of the dynamic electricity prices is lower than the influence of the
TOU prices. Consequently, we can assume that the households of our treatment group have started to
change their electricity usage behavioral patterns due to the introduction of the dynamic prices.
In order to determine during which times of the day the dynamic price is influencing electricity usage
behavior, we have run 24 different panel data regressions to get a better overview. Our results have
shown that the dynamic prices are negatively influencing relative usage behavior between 8AM and
5PM. We can therefore imply that the households of our sample are capable of, or willing to, change
their electricity usage behavior based on price fluctuations during these times but not during other
times of the day. As the time window in which households are capable to change usage behavior is
overlapping with the time window in which we were able to observe deviations in behavioral patterns
between the treatment and the control group, we can imply that the dynamic prices have encouraged
load shifting behavior in our sample.
53
Master Thesis – Navid Sadat-Razavi
Several unexpected findings have been made during this analysis. First, despite almost all other studies
suggesting peak times to be the most crucial times for households to save energy as a response to price
signals, there is only a partially significant influence of the dynamic prices during peak times (5PM7PM) at 5PM (Schleich & Klobasa, 2013; Allcott, 2011; Bartush et al., 2011). However, this study has
observed changes in the relative usage rather than the absolute usage, which enables us to make
statements about behavioral patterns but not total consumption. Hence, we have found that residential
households are not changing behavioral electricity usage patterns after 5PM. Additionally, we expected
an insignificant relationship between the usage behavior during day times and electricity prices due to
the absence of individuals in many households. However, our results are opposite. The participating
households were especially capable of changing their electricity usage behavior during these times. One
possible explanation is that individuals staying at home during the day, while other members are leaving
the household, might feel a higher sense of control over their electricity usage and the overall impact
of their behavior, and have therefore properly reacted to price signals during these times.
We have seen that the influence of household attributes on the willingness to use electricity is significant,
but varying throughout the day. Additionally, households are only capable of changing their electricity
usage patterns during specific moments of the day. Hence, it reasonable to assume that the willingness
to use electricity and behavioral change based on changes in electricity prices are closely related
to each other. Consequently, in order to sufficiently target residential households for energy services in
dynamic pricing settings, utility providers need to find a way to properly segment these households by
objective variables such as household attributes in a way that reflect the households’ capability to
change electricity usage patterns. Additionally, this would provide evidence for our assumption that
household attributes reflect lifestyle patterns of electricity consumers. The third part of our study has
aimed to do so.
4.3 Clustering Usage Behavior Based on Household Attributes
Our PCA analysis has found that out of all available variables of our sample, the building size, and type,
the number of occupants and the terrain type are the most significant when properly segmenting the
households of our study. Five clearly distinguishable clusters have been found, out of which the first
cluster is disregarded due to its size of two households. The second cluster is characterized by having
the biggest building size despite the lowest number of occupants. Additionally, this cluster has the
highest share of urban household locations. It is likely, though not provable, that the combination of
highest building size with lowest number of occupants in urban areas is an indication for a high income
group. Moreover, the third cluster is characterized by having the highest number of ‘detached’ houses,
which are actual stand-alone houses. Additionally, this cluster has the highest amount of households
living in rural areas. Hence, this cluster can be characterized by its rural locations and independent
housing. The fourth cluster has the highest amount of household occupants despite having the smallest
household sizes, which are primarily semi-detached houses. The share of urban and suburban
households is also high for this cluster. We can therefore assume that low- and mid-income families
from urban and sub-urban areas are represented by this group. Lastly, cluster five cannot be concretely
defined, as all variables are showing evenly distributed results.
In the last step, we have investigated how capable these households are in changing their behavioral
patterns. Our results show that clusters two and three are showing clearly distinguishable behavioral
electricity usage patterns from the rest of our sample. Two conclusions can be made from these findings.
54
Master Thesis – Navid Sadat-Razavi
First, by segmenting households based on their household attributes, we were able to isolate two groups
that are potentially engaging with the introduced dynamic prices by changing their behavioral patterns.
We can claim that cluster 2 reduced their load during the day, while cluster 3 engaged in load shifting
behavior. Second, we can claim that our cluster analysis confirms our assumption that household
attributes reflect electricity usage lifestyle patterns.
4.4 Bringing the Findings Together
Our results have shown that changes in behavioral electricity usage patterns, based on the introduction
of hourly changing electricity prices, have occurred between 8AM and 5PM. As we have pointed out in
section 4.1, the willingness to use electricity increases strongly during evening times. This suggests that
households during the evening are less price sensitive, and therefore less likely to change behavioral
patterns as a response to dynamic electricity prices. One reason for the decreased price sensitivity could
be that individuals perceive their impact on the households’ total electricity usage as weak in households
with higher numbers of occupants, or bigger buildings and are therefore less responsive to the prices.
Another valid explanation would be that electricity usage becomes more preferable during evening
times, and therefore increases the willingness to consume electricity at higher prices.
Additionally, the observation that household attributes have a lower explanatory power when dynamic
prices are introduced was made. Especially during the specific times in which the dynamic prices have
shown a significant influence on electricity usage patterns, the explanatory power of our household
attributes has either decreased or was diminished. Sticking to an earlier assumption that the household
attributes of our study reflect lifestyle patterns and habits, we can suggest that the introduction of hourly
changing prices has encouraged households to change or break their behavioral patterns. Hence, moving
towards a more rational decision-making process. The outcome of our household attributes-based cluster
analysis can partly support the assumption that our household attributes reflect customer patterns, as two
clusters have been found that substantially differentiate themselves in their behavioral electricity usage
patterns.
To conclude, we have observed that households have changed their behavioral patterns as a response to
dynamic prices. It is possible to claim that the increased level of communicated information in the form
of hourly prices that reflect real-world electricity market situations has enabled the households to do so.
Perhaps, the additional information has increased energy usage awareness and a sense of higher
individual impact. As a result, future energy services should provide more fine-grained information
about energy use, ideally on an individual level rather than a household level, in order to unlock higher
customer responsiveness. This applies to the general provision of information, as well as dynamic
electricity prices (e.g. personalized prices). A possible consequence would be that customers will change
their patterns also during other times of the day, after 5PM. Additionally, dynamic price variations could
be increased in order for households to deem electricity prices as relevant. Essentially, all of these
recommendations for energy services are addressing the need for utility providers to communicate a
more comprehensive and complete picture of the energy sector and energy usage to residential
households, thereby encouraging households to move towards a more rational decision making process.
However, in the case that all residential households will receive dynamic electricity prices and everyone
starts to change its consumption behavior based on these prices, the price variations on the APX
electricity market will become flatter, as demand during low-price times increases and demand during
high-prices times decreases. Consequently, this would reverse the effect of dynamic electricity prices
55
Master Thesis – Navid Sadat-Razavi
based on electricity market prices at least during the time of 8AM to 5PM, and households are likely to
go back to previous habitual patterns. Therefore, future energy services should also focus on nonmonetary incentives and an increased level of information provided to households. Additionally, future
Demand Response Programs should explore the options to use personalized or artificially varying
electricity prices to engage their customers in a dialogue.
4.5 Limitations and Recommendations for Future Research
For several reasons, the exact magnitude of household attributes, dynamic prices and household
electricity usage might not generalize to other settings and time periods.
The participants of the ‘Qurrent Energie’ project were a selected group of people who are potentially
more price elastic than the general population. Moreover, the variance in hourly electricity prices during
the course of the project is lower than the variance of electricity prices used in other experiments. This
is especially important because a higher price variation has been proven to increase potential gains from
dynamic prices and because higher price uncertainties could encourage consumers to devote more
attention to prices, which would have an impact on electricity usage (Allcott, 2011; Faruqui & Palmer,
2012; Schleich & Klobasa, 2013). It is likely that in reality households will only accept to change their
usage patterns based on prices if the effect on the electricity bill can provide meaningful savings.
Additionally, our study was subject to several limitations that should be addressed in future research.
The absolute electricity usage of the households during the ‘Qurrent Energie’ project was recorded as
consumption subtracted by PV production. It is essential for more accurate examinations to investigate
the influence on pure consumption, in order to fully understand consumption behavior and price
sensitivity.
Moreover, we cannot claim with absolute confidence that the behavioral patterns of our households have
changed due to the introduction of dynamic pricing signals, as our study was missing pre-treatment data
for the analysis. Future studies should therefore include pre-treatment data in order to prove changes in
behavioral patterns with absolute confidence. Other studies on this topic have suggested to follow a
difference-in-differences analysis, in order to arrive at robust findings (Di Cosmo et al., 2014).
Furthermore, we have found that the importance of household attributes as determinants of a household’s
energy usage patterns is decreasing. Hence, future research needs to explore what other types of
variables have become more relevant for future dynamic pricing environments. Previous research has
suggested a broad set of different variable types that can be explored, such as socio-economic
information (e.g. education, income, occupation, richness of neighborhood, social grade), appliance
ownership (e.g. electric vehicles, electric heating, other electric appliances) and belief systems (e.g.
environmental awareness, motivation to change). Additionally, other than recommended, our
segmentation is not dynamic but static. Future research needs to develop a dynamic segmentation model
based on various variable types like socio-demographic information and appliance ownership variables
in order to determine the most effective segmentation approach for each hour separately.
Lastly, in the computation of our dependent variable ‘Willingness to Use Electricity’ we are assuming
that households are capable to change their entire load based on electricity prices. In reality, only a minor
part of the consumption load is elastic. One way to solve this issue is to calculate how much of a
household’s load is elastic by gathering more detailed information about the electrical appliance
56
Master Thesis – Navid Sadat-Razavi
ownership of the households. One example of such an approach is the MIT RED database, which is
releasing how much percentage of elastic load certain types of households have.
57
Master Thesis – Navid Sadat-Razavi
5. Conclusion
5.1 General Conclusion
The main aim of our study was to examine customer patterns of residential households in a dynamic
price setting. With the development of new technologies such as smart meters, utility providers have the
opportunity to inherently change the way they are communicating with their customers. One way the
utility providers are aiming to improve the interaction with their customers is by introducing
dynamically changing electricity prices, based on real electricity market prices. However, less is
currently known about consumer preferences and response to constantly changing electricity prices.
Given that different people can have a diverse set of values and preferences, it is assumed that their
reaction to changing electricity prices is quite diverse. Therefore, we have asked ourselves how
household characteristics and dynamic electricity prices influence household electricity consumption in
dynamic price settings. We believe that clarifying the relationship of these two components with
electricity consumption behavior can deliver valuable insights for future energy services.
Our findings have shown that the price sensitivity of households exposed to technology-enabled
dynamic price settings is higher as compared to household exposed to the usual Time-of-Use pricing
applied to many households today. Moreover, this price sensitivity is significantly influenced by
household attributes, such as the number of occupants, the building size, age and type, and the
availability of roof insulation. This finding suggests that household attributes can be a useful tool to
determine how likely households are to properly respond to electricity prices in general. In addition, we
have found that the significance of these household attributes is varying during different hours of the
day. Hence, future efforts to determine household price sensitivity have to evaluate price sensitivity on
an hourly basis, based on different sets of variables. Lastly, we have found that, other than the
availability of roof insulation, all household attributes have shown a weaker relationship with a
household’s willingness to use electricity compared to our control group. This gives reason to believe
that households exposed to dynamic prices have generally become more price sensitive and follow fever
habitual patterns. A closer look at the influence of the dynamic prices was therefore essential.
Our findings have shown that our treatment group had a different relative load profile than our control
group, which suggests that the dynamic prices have influenced electricity usage patterns in a way that
the TOU prices have not. The analysis of this study has confirmed that dynamic prices have a
significantly different, in our case less positive, relationship with electricity usage behavior compared
to TOU prices. Although the relationship of electricity prices with relative usage is positive in our
analysis, we believe that the excitement of the participants of our study is responsible for this. Hence,
the inclusion of a data set with a longer time-period would probably show negative correlations.
Therefore, the influence dynamic prices should normally be stronger, or more negative, than the
influence of TOU prices. Moreover, our results are able to prove that the capability of households to
change their consumption behavior based on changes in electricity prices only exists between 8AM to
5PM. Lastly, we have found that the time window in which household are capable to change their
behavior based on electricity prices is overlapping with the time-window in which the relative usage
between treatment and control group is deviating from each other. We have interpreted this observation
as instances of dynamic pricing encouraged load shifting behavior.
58
Master Thesis – Navid Sadat-Razavi
Based on the previous two analyses, we have come to the conclusion that efforts to properly segment
households into groups that reflect a household’s capability to change electricity usage patterns can be
based on objective variables, such as the household attributes used in our study. Our findings show that
this approach was successful in identifying two specific groups of households that have changed their
electricity usage behavior, and are engaging in load shifting. Although we have proposed to follow a
segmentation strategy that takes a set of different variables into account for every hour of the day, we
have followed a static segmentation for practical reasons.
5.2 Managerial Implications
Utility providers are starting to introduce dynamic prices to their energy contracts with residential
households, with the aim to accurately match fluctuating energy supply with increasingly uncertain
demand patterns. Due to the introduction of smart metering devices, this has become realizable.
However, in order to properly take advantage of this improved way of communicating, it has to be
understood how different types of households react to prices in a dynamic pricing setting.
First, utility providers need to understand how much a household will respond to price variations. Hence,
it is crucial to grasp what factors influence the price sensitivity of households. As the consumer choice
theory correctly explains, households will make a purchasing decision based on budget constraints
(electricity price), time constraints (their daily routine) and preferences. In order to create a complete
picture of the price sensitivity of households, it is therefore crucial to examine the influence of household
attributes, as these will reflect many of the preference of households and the development of the price
sensitivity throughout the day. Our results show that utility providers have to examine price sensitivity
indeed for every hour of the day and base their evaluation on a constantly changing set of variables.
Hence, future customer targeting initiatives should segment households into dynamic groups based on
a set of variables that proves to be significant on that exact hour of the day. In order to encourage a better
interaction after 5PM, non-monetary incentives should be developed. In specific, utility providers should
examine the influence of different types of informational feedback to specifically target household
groups. This can be done in the form of gamification, consumption comparisons and other concepts
suggested in previous studies (Hermsen et al., 2016; Darby, 2006; Fischer, 2008). Based on our findings,
we suggest that non-monetary incentives such as informational feedback should also be examined on an
hourly level, to properly evaluate the performance throughout the day. Lastly, our study has shown that
utility providers would be well advised to find out as much information about their consumers as
possible, in order to improve and extend our current line of variables. For example, socio-demographic
information, such as education, income level or appliance ownership information have been proven to
significantly influence price responsiveness (Hayn et al., 2014).
5.3 Academic Implications
Our study has attempted to stress that electricity consumers are neither rational decision makers, nor
basing their consumption behavior on entirely irrational decisions. We have successfully proven that a
mix of situational circumstances, budget constraints, time constraints and preferences play an important
role while households make consumption decisions. Moreover, we have stressed the fact that irrational
behavior comes into play when examining energy usage behavior in the form of habits.
59
Master Thesis – Navid Sadat-Razavi
Advanced metering techniques have the potential to encourage rational decision making based on prices
and the change of behavioral patterns based on disruptive information, which can contribute to success
of future energy services.
We have successfully proven that household attributes are significant determinants of household price
sensitivity, as they are able to reflect a household daily electricity usage patterns. Furthermore, we have
proven that dynamic prices significantly influence the relative usage profile of households and thus
encourage households to break behavioral patterns in favor of cheaper electricity.
Moreover, another significant contribution of our study is the notion that future studies examining
electricity consumption and behavior, have to fine-grain their analysis at least to an hourly level. Almost
all scientific analyses have disregarded this fact and aggregated their results to a daily or peak/off-peak
level, which does not do justice to the complex constraints residential household are facing throughout
the day.
Another significant contribution is that we have successfully proven that dynamic prices influence
electricity usage behavior in a significantly different way compared to TOU prices, and actually
encourage load shifting behavior, which could not be proven by various previous studies investigating
TOU prices.
60
Master Thesis – Navid Sadat-Razavi
6. References
European Parliament, Council of the European Union. (2009). Directive 2009/28/EC of the european
parliament and of the council of 23 April 2009 on the promotion of the use of energy from
renewable sources and amending and subsequently repealing Directives 2001/77/EC and
2003/30/EC. Official Journal of the European Union European Union.
Abdi, H., & Williams, L. (2010). Principle Component Analysis. WIREs Comp Stat, 2(4), 433-459.
Accenture. (2015). The New Energy Consumer Balancing Strategic and Operational Imperatives.
Accenture.
Albadi, M., & El-Saadany, E. (2007). Demand Response in Electricity Markets: An Overview. Power
Engineering Society General Meeting, 1-5; 24-28.
Albadi, M., & El-Saadany, E. (2008). A summary of demand response in electricity markets. Electric
Power Systems Research, 78(1), 1989-1996.
Alberini, A., Gans, W., & Velez-Lopez, D. (2011). Residential Consumption of gas and electricity in
the U.S: The role of prices and income. Energy Economics, 33(1), 870-881.
Allcott, H. (2011). Rethinking real-time electricity pricing. Resource and Energy Economics, 33(1),
820-842.
Anderberg, M. (1973). Cluster Analysis for Applications (1 ed.). New York: Academic Press.
APX Group. (2016). APX power spot exchange. Retrieved July 08, 2016, from www.apxgroup.com/apxpower-nl/
Baltagi, B. (2013). Econometric Analysis of Panel Data (5 ed.). United States: John Wiley & Sons.
Bartush, C., Wallin, F., Odlare, M., Vassileva, I., & Wester, L. (2011). Introducing a demand-based
electricity distribution tariff in the residential sector: Demand response and customer perception.
Energy Policy, 39, 5008-5025.
Bhatia, S. (2004). Adaptive K-Means Clustering. University of Missouri - St.Louis. American
Association for Artificial Intelligence.
Bhattacharya, K., Bollen, M., & Daalder, J. (2001). Operation of Restructured Power System. London:
Kluwer Academics Publishers.
Bloustein, E. (2005). Assessment of Customer Response to Real Time Pricing (1 ed.). New Jersey:
Rutgers - The State University of New Jersey.
Borenstein, S., Jaske, M., & Rosenfeld, A. (2002). Dynamic Pricing, Advanced Metering, and Demand
Response in Electricity Markets. Los Angeles, CA: The Energy Foundation.
Brandon, G., & Lewis, A. (1999). Reducing Household Energy Consumption: A Qualitative and
Quantitative Field Study. Journal of Environmental Psychology, 19(1), 75-85.
Capgemini Consulting. (2015). European Energy Markets Observation. Paris: Capgemini.
Contreras, J., Asensio, M., Quevedo, P., Munoz-Delgado, G., & Montoya-Bueno, S. (2016). Joint RES
and Distribution Network Expansion Planning under a Demand Response Framework (1 ed.).
London: Elsevier Academic Press.
Darby, S. (2006). The Effectiveness of Feedback on Energy Consumption: A Review for DEFRA of the
Literature on Metering, Billing and Direct Displays. University of Oxford. Oxford:
Environmental Change Institute, University of Oxford.
Darby, S. (2012). Literature Review for the Energy Demand Research Project. Oxford: Environmental
Change Institute - University of Oxford.
61
Master Thesis – Navid Sadat-Razavi
Darby, S. (2012). Literature Review for the Energy Demand Research Project. Oxford: Environmental
Change Institute - University of Oxford.
Deaton, A., & Muellbauer, J. (1960). An almost ideal demand system. The American Economic Review,
70(3), 312-326.
DeMeo, E., Jordan, G., & Kalich, C. (2007). Accommodating wind’s natural behavior. IEEE Power and
Energy Magazine, 5(6), 59-67.
Di Cosmo, V., Lyons, S., & Nolan, A. (2014). Estimating the impact of time-of-use pricing on Irish
electricity demand. The Energy Journal, 35(2), 1-26.
Ehrhardt-Martinez, K., Laitner, J. A., & Donnelly, K. A. (2011). Chapter 10—Beyond the Meter:
Enabling Better Home Energy Management. In S. Fereidoon Perry, Energy, Sustainability and
the Environment (pp. 273-303). Boston: Butterworth- Heinemann.
Elliot, A. J., & Covington, M. V. (2001). Approach and avoidance motivation. Educational Psychology
Review, 13(2), 73-92.
European
Union.
(2016).
Cop21.gouv.fr.
Retrieved
http://www.cop21.gouv.fr/en/learn/what-is-cop21/
January
22,
2016,
from
Eurostat. (2015). europe.eu. Retrieved Januar 22, 2016, from http://ec.europa.eu/eurostat/statisticsexplained/index.php/Consumption_of_energy
Farhangi, H. (2010). The path of the smart grid. Power and Energy Magazine, IEEE, 8(1), 18-28.
Farhar, B. (1999). Willingness to Pay for Electricity from Renewable Resources: A Review of Utility
Market Research. Office of Scientific and Technical Information (OSTI).
Faruqui, A., & Malko, J. (1983). The Residential Demand for Electricity By Time-Of-Use: A Survey of
Twelve Experiments With Peak Load Pricing. Energy, 8(10), 781-795.
Faruqui, A., & Palmer, J. (2012). The Discovery of Price Responsiveness - A Survey of Experiments
involving Dynamic Pricing of Electricity. SSRN Electronic Journal, 4(1), 1-14.
Faruqui, A., Sergici, S., & Sharif, A. (2010). evidence, The impact of informational feedback on nergy
consumption - A survey of the experimental. Energy, 35(4), 1598-1608.
Faruqui, A., Sergici, S., & Sharif, A. (2010). The impact of informational feedback on nergy
consumption - A survey of the experimental evidence. Energy, 35(4), 1598-1608.
Filippini, M. (2011). Short- and long-run time-of-use price elasticities in Swiss residential electricity
demand. Energy Policy, 39(1), 5811-5817.
Fischer, C. (2008). Feedback on Household Electricity Con- sumption: A Tool for Saving Energy?
Energy Efficiency, 1(1), 79-104.
Fisher, C. (2007). Discussion Paper 8: Influencing Electricity Consumption Via Consumer Feedback:
A Review of Ex- perience. Berlin: ECEEE 2007 Summer Study.
Gardner, B. (2014). A review and analysis of the use of ‘Habit’ in understanding, predicting and
influencing health-related behavior. Health Psychology Review, 8(1), 1-19.
Ghose, A., Goldfarb, A., & Han, S. (2013). How is the Mobile Internet Different? Search Costs and
Local Activities. Information Systems Research, 1-19.
Greene, W. (2008). Econometric Analysis (5 ed.). New Jersey: Pearson Education.
Grijalva, S., & Tariq, M. (2011). Prosumer-based smart grid architecture enables a flat, sustainable
electricity industry. Innovative Smart Grid Technologies (ISGT), 2011 IEEE PES, 1-6; 17-19.
Hayn, M., Bertsch, V., & Fichtner, W. (2014). Electricity load profiles in Europe: The importance of
household segmentation. Energy Research & Social Science, 3(1), 30-45.
62
Master Thesis – Navid Sadat-Razavi
Hermsen, S., Frost, J., Renes, R., & Kerkhof, P. (2016). Using feedback through digital technology to
disrupt and change habitual behavior: A critical review of current literature. Computers in
Human Behavior, 57, 61-74.
Higgins, E. T. (1997). Beyond pleasure and pain. American psychologist, 52(12), 1280.
Hino, H., Shen, H., Murata, N., Wakao, S., & Hayashi, Y. (2013). A versatile clustering method for
electricity consumption pattern analysis in households. IEEE Transactions on Smart Grid, 4(2),
1048-1057.
Hutton, R. B., Mauser, G. A., Filiatrault, P., & Ah- tola, O. T. (1986). Effects of Cost-Related Feedback
on Consumer Knowledge and Consumption Behavior: A Field Experimental Approach. Journal
of Consumer Research, 13(3), 327-336.
Ingham, A., Levinger, G., Graves, J., & Peckham, V. (1974). The Ringelmann effect: Studies of group
size and group performance. Journal of Experimental Social Psychology, 10, 371-384.
International Business Publications. (2016). Netherlands Investment and Business Guide Volume 1
Strategic and Practical Information. IBP, Inc.
Jacobsson, S., & Bergek, A. (2004). Transforming the energy sector: the evolution of technological
systems in renewable energy technology. Industrial and Corporate Change, 13(5), 815-49.
Jager, W. (2003). Breaking ‘bad habits’: a dynamical perspective on habit formation and change. In H.
L., W. Jager, & L. Steg, Human decision-making and environmental PerceptioneUnderstanding and assisting human decision-making in real life settings. Groningen:
University of Groningen.
Jain, R. K., Taylor, J. E., & Peschiera, G. (2012). Assessing eco-feedback interface usage and design to
drive energy efficiency in buildings. Energy and Buildings, 48, 8-17.
Kavousian, A., Rajagopal, R., & Fischer, M. (2013). Determinants of residential electricity
consumption: using smart meter data to examine the effect of climate, building characteristics,
appliance stock, and occupants behavior. Energy, 55, 184-194.
Kwac, J., Flora, J., & Rajagopal, R. (2014). Household Energy Consumption Segmentation Using
Hourly Data. IEEE Transactions on Smart Grid, 5(1), 420-430.
Lampropoulos, Vanalme, & Kling. (2010). A methodology for modeling the behavior of electricity
prosumers within the smart grid. Innovative Smart Grid Technologies Conference Europe (ISGT
Europe), 1-8.
Matsukawa, I. (2001). Household Response to Optional Peak-Load Pricing of Electricity. Journal of
Regulatory Economics, 20(3), 249-267.
Matthies, E. (2005). Wie können PsychologInnen ihr Wissen besser an die PraktikerIn bringen?
Vorschlag eines neuen, integrativen Einflussschemas umweltgerechten Alltagshandelns.
Umweltpsychologie, 9(1), 62-81.
McCalley, L. T., & Midden, C. J. (2002). Energy Conservation through Product-Integrated Feedback:
The Roles of Goal-Setting and Social Orientation. Journal of Economic Psychology, 23(5), 589603.
McCarney, R., Warner, J., Iliffe, S., van Haselen, R., Griffin, M., & Fisher, P. (2007). The Hawthorne
Effect: a randomised, controlled trial. BMC Medical Research Methodology, 7(30), 1-8.
McLoughlin, F., Duffy, A., & Conlon, M. (2012). Characterising Domestic electricity consumption
patterns by dwelling and occupant socio-economic variables: an Irish case study. Energy Build,
48(2), 240-248.
63
Master Thesis – Navid Sadat-Razavi
Midden, C. J., Meter, J. E., Weenig, M. H., & Zieverink, H. J. (1983). Using Feedback, Reinforcement
and Information to Reduce Energy Consumption in Households: A Field-Experiment. Journal
of Economic Psychology, 3(1), 65-86.
Murthy Balijepalli, V. S., Vedanta Pradhan, S. A., & Shereef, R. M. (2011). Review of Demand
Response under Smart Grid Paradigm. India: 2011 IEEE PES International Conference on
Innovative Smart Grid Technologies-India.
Nababan, T. (2015). Analysis of Household Characteristics Affecting the Demand of PLN’s Electricity.
An Observation on Small Households in City of Medan, Indonesia. Academic Journal of
Economic Studies, 1(2), 79–92.
National Research Council of the National Academies. (2006). Surface Temperature Reconstructions
for the last 2000 years. Washington D.C.: The National Academies Press.
Northcraft, G. B., Schmidt, A. M., & Ashford, S. J. (2011). Feedback and the rationing of time and effort
among competing tasks. Journal of Applied Psychology, 96(5), 1076.
Palensky, P., & Dietrich, D. (2011, August). Demand Side Management: Demand Response, Intelligent
Energy Systems, and Smart Loads. IEEE Transactions on industrial informatics, 7(3), 381-388.
Peeples, M. (2011). R Script for K-Means Cluster Analysis. Retrieved July 10, 2016, from
http://www.mattpeeples.net/kmeans.html
Salvatore. (2008). Consumer Preferences and Choice. In Microeconomics: Theory and Applications (pp.
57-86). Oxford: Oxford University Press.
Santer, Taylor, Wigley, Johns, Jones, Karoly, . . . Stouffer. (1996). A search for human influences on
the thermal structure of the atmosphere. Nature(382), 39-46.
Schleich, J., & Klobasa, M. (2013). How much shift in demand? Findings from a field experiment in
Germany. ECEEE 2013 SUMMER STUDY – RETHINK, RENEW, RESTART, 1(1), 1919-1925.
Seligman, C., Becker, L. J., & Darley, J. M. (1981). Encouraging Residential Energy Conservation
through Feedback. Advances in Environmental Psychology, 3, 93-113.
Sexton, R., Johnson, N., & Konakayama, A. (1987). Consumer response to continuous-display
electricity-use monitors in a time-of-use pricing experiment. Journal of Consumer Research,
14(1), 55-62.
Spiegel.
(2014).
Spiegel.de.
Retrieved
January
22,
2016,
from
http://www.spiegel.de/wirtschaft/unternehmen/rwe-meldet-2-8-milliarden-euro-verlust-fuer2013-a-956749.html
Statistik Austria. (2011). Modellierung des Stromverbrauchs in den privaten Haushalten Österreichs
nach unterschiedlichen Verwendungszwecken. Vienna: Statistik Austria.
Sundramoorthy, V., Cooper, G., Linge, N., & Qi, L. (2011). Domesticating Energy-Monitoring Systems:
Challenges and Design Concerns. EEE on Pervasive Computing, 10(1), 20-27.
The
Economist.
(2013).
Economist.com.
Retrieved
January
22,
2016,
from
http://www.economist.com/news/briefing/21587782-europes-electricity-providers-faceexistential-threat-how-lose-half-trillion-euros
Torriti, J., Hassen, M., & Leach, M. (2010). Demand response experience in Europe: Policies,
prgrammes and implementation. Energy, 35, 1575-1583.
U.S. Energy Information Administration. (2016). World energy demand and economic outlook.
International Energy Outlook 2016.
US Department of Energy. (2006). Benefits of Demand Response in Electricity Markets and
Recommendations for Achieving them. Washington D.C.: Report to the United States Congress.
64
Master Thesis – Navid Sadat-Razavi
Van Werven, M., & Scheepers, M. (2005). The Changing Role of Energy Suppliers and Distribution
System Operators in the Deployment of Distributed Generation in Liberalised Electricity
Markets. Amsterdam: Energy Research Center of the Netherlands.
Vine, D., Buys, L., & P., M. (2013). The Effectiveness of Energy Feedback for Conservation and Peak
Demand: A Literature Review. Open Journal of Energy Efficiency, 2, 7-15.
Wallenborn, G., Orsini, M., & Vanhaverbeke, J. (2011). House- hold Appropriation of Electricity
Monitors. International Journal of Consumer Studies, 35(2), 146-152.
Westerman, G., Bonnet, D., & A., M. (2014). Leading Digital: Turning Technology into Business
Transformation. New York: Harvard Business Review Press.
Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology into Business
Transformation. New York: Harvard Business Review Press.
Wilhite, H., & Ling, R. (1995). Measured Energy Savings from a More Informative Energy Bill. Energy
and Buildings, 22(2), 145-155.
Winett, R., Kagel, J., & Battalio, R. (1978). Effects of Monetary Rebates, Feedback, and Infor- mation
on Residential Electricity Conservation. Journal of Applied Psychology, 63(1), 73-80.
Wood, G., & Newborough, M. (2003). Dynamic Energy-Con- sumption Indicators for Domestic
Appliances: Environ- ment, Behaviour and Design. Energy and Buildings, 35(8), 821-841.
Wood, W., & Neal, D. T. (2009). The habitual consumer. Journal of Consumer Psychology, 19(4), 579592.
Yohannis, Y., Mondol, J., Wright, A., & Norton, B. (2008). Real-Life energy use in the UK: how
occupancy and dwelling characteristics affect domestic electricity use. Energy Build, 40(6),
1053-1059.
65
Master Thesis – Navid Sadat-Razavi
7. Appendix
Summary Statistics – Treatment Group
N
Mean
Median
Min
Max
47275
0,042
0,0343
-0,00412
1
price1
720
0,1676
0,1682
0,1515
0,1765
price2
720
0,1664
0,167
0,1504
0,1725
price3
720
0,1655
0,1657
0,1493
0,1711
price4
720
0,1652
0,1662
0,1478
0,1696
price5
720
0,1664
0,1679
0,149
0,1711
price6
720
0,172
0,1732
0,1485
0,1825
price7
720
0,1773
0,1798
0,1487
0,1876
price8
720
0,1777
0,1803
0,1463
0,1896
price9
720
0,1781
0,179
0,1627
0,1913
price10
720
0,1762
0,1763
0,1631
0,1891
price11
720
0,1749
0,1759
0,1631
0,1851
price12
720
0,1734
0,1733
0,1611
0,1844
price13
720
0,1718
0,1718
0,1585
0,1827
price14
720
0,1702
0,1709
0,1536
0,1804
price15
720
0,1697
0,1698
0,1596
0,1789
price16
720
0,1703
0,1707
0,1537
0,1789
price17
720
0,1744
0,1751
0,1622
0,1827
price18
720
0,1812
0,1807
0,162
0,1999
price19
720
0,185
0,1862
0,1653
0,1994
price20
720
0,1797
0,1792
0,1666
0,1915
price21
720
0,1747
0,1744
0,1647
0,1836
price22
720
0,1736
0,1741
0,1638
0,1795
price23
720
0,1708
0,1714
0,1625
0,1807
price24
720
0,1696
0,1692
0,1503
0,1925
720
37,74
1
0
259
relative consumption
Solar Influx
Table 13. Summary Statistics of Prices and Relative Usage
Summary Statistics - Control Group
N
Mean
Median
Min
Max
103515
0,04005
0,03279
-0,004
1
price1
1
0,1744
0,1744
0,1744
0,1744
price2
1
0,1868
0,1868
0,1868
0,1868
0
259
relative consumption
SolarInflux
720
37,74
1
Table 14. Summary Statistics of Prices and Relative Usage
66
Master Thesis – Navid Sadat-Razavi
Panel Data Regression Results – Treatment Group
Dependent variable: Willingness to use electrical energy (usage/price)
Y
Persons
Building
Age
Building
Size
Building
Type
Roof
Insulation
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.33***
0.19***
0.16***
0.14***
0.16***
0.28***
0.56***
(0.05)
(0.04)
(0.04)
(0.03)
(0.03)
(0.04)
(0.05)
**
**
*
***
**
-0.15
***
-0.10
-0.09
(0.05)
(0.04)
(0.04)
***
***
***
0.01
0.01
0.02
-0.15
***
(0.03)
0.003
***
-0.15
***
(0.03)
0.003
***
-0.07
(0.04)
0.003
***
0.14
Observatio
ns
R
2
Adjusted
R2
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
0.27***
0.16**
0.21***
0.39***
0.57***
0.63***
0.79***
0.70***
0.69***
0.76***
0.81***
0.81***
0.82***
0.71***
0.66***
0.61***
0.39***
(0.05)
(0.07)
(0.08)
(0.09)
(0.10)
(0.10)
(0.09)
(0.08)
(0.07)
(0.07)
(0.06)
(0.07)
(0.06)
(0.06)
(0.06)
(0.06)
(0.05)
***
***
0.08
0.01
-0.03
-0.12
(0.05)
0.002
(0.05)
***
-0.48
***
(0.07)
-0.59
***
(0.08)
-0.76
***
(0.09)
-0.92
***
(0.10)
0.001
0.001
-0.001
-0.001
-0.003
*
-0.94
***
(0.10)
-0.003
-0.86
***
(0.09)
-0.69
***
(0.08)
*
0.0001
0.002
-0.54
***
(0.07)
0.003
**
-0.27
***
(0.07)
0.004
***
-0.05
0.10
(0.07)
(0.07)
(0.06)
(0.06)
(0.06)
(0.06)
(0.05)
***
***
***
***
***
***
0.01***
0.01
0.01
0.18
0.01
0.15
0.01
0.01
0.01
(0.001)
(0.001)
(0.001)
(0.001)
(0.005)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.002)
(0.002)
(0.002)
(0.002)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
-0.06
-0.06
-0.14**
-0.20***
-0.16***
-0.15***
-0.04
-0.005
0.02
0.01
0.04
-0.05
-0.02
-0.19**
-0.25***
-0.31***
-0.07
-0.03
-0.07
-0.08
-0.09
-0.02
0.09
-0.002
(0.05)
(0.04)
(0.04)
(0.03)
(0.03)
(0.04)
(0.05)
(0.05)
(0.07)
(0.08)
(0.09)
(0.10)
(0.10)
(0.09)
(0.08)
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
(0.06)
(0.06)
(0.06)
(0.05)
-0.09
-0.38
**
**
**
***
0.27
-0.31
*
-0.25
(0.17)
(0.17)
(0.20)
(0.19)
(0.16)
-0.18
-0.13
0.01
0.16
(0.16)
(0.14)
(0.12)
(0.10)
0.19
**
(0.09)
0.24
*
(0.13)
Solar
Influx
Constant
(9)
-0.1
***
-0.53
(0.23)
-0.01
***
-0.79
***
(0.26)
-0.02
***
-1.23
***
(0.30)
-0.02
***
-1.20
***
(0.32)
-0.02
***
-1.47
***
(0.32)
-0.02
***
-0.99
***
(0.30)
-0.02
***
-0.95
***
(0.25)
-0.02
***
-0.38
*
(0.22)
-0.02
***
*
0.16
0.40
(0.22)
(0.21)
-0.02
***
-0.05
**
0.42
*
(0.22)
0.42
(0.21)
0.55
(0.20)
0.36
(0.003)
(0.002)
(0.002)
(0.002)
(0.002)
(0.002)
(0.002)
(0.002)
(0.003)
(0.005)
(0.02)
0.44
0.53
1.51***
1.39***
0.75**
0.68
-0.001
3.10***
5.38***
7.39***
7.51***
7.89***
8.65***
6.49***
6.06***
3.52***
2.08**
0.32
(0.52)
-0.15
-0.72
-0.02
0.51
-0.12
0.18
(0.51)
(0.46)
(0.39)
(0.34)
(0.31)
(0.42)
(0.58)
(0.60)
(0.81)
(0.94)
(1.04)
(1.09)
(1.13)
(1.08)
(0.92)
(0.79)
(0.81)
(0.84)
(0.73)
(0.69)
(0.65)
(0.65)
(0.60)
(0.56)
1,990
1,923
1,987
1,991
1,991
1,991
1,990
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,991
1,999
2,100
0.12
0.07
0.02
0.05
0.06
0.04
0.07
0.03
0.05
0.07
0.08
0.09
0.09
0.10
0.10
0.09
0.08
0.10
0.10
0.13
0.11
0.10
0.15
0.13
0.12
0.07
0.02
0.05
0.06
0.04
0.07
0.03
0.05
0.06
0.08
0.09
0.09
0.10
0.10
0.09
0.08
0.10
0.10
0.13
0.11
0.10
0.15
0.13
0.16
0.15
0.22
0.08
0.09
0.06
0.09
0.10
0.10
0.11
0.12
0.11
0.11
0.15
0.14
0.15
0.11
0.11
0.12
0.14
0.06
0.06
0.07
0.08
Hausman
(p>chi2)
F Statistic
44.94***( 23.63***( 7.86***( 17.64***( 21.00***( 15.22***( 25.33***( 7.96***( 14.13***( 19.76***( 25.28***( 27.88***( 28.67***( 29.98***( 31.26***( 29.59***( 25.23***( 32.48***( 30.67***( 49.79***( 42.30***( 35.68***( 60.02***( 51.44***(
df = 6;
df = 6; df = 6; df = 6;
df = 6;
df = 6;
df = 6; df = 7; df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 6;
df = 6;
df = 6;
df = 6;
df = 6;
1983)
1916)
1980)
1984)
1984)
1984)
1983)
1983)
1983)
1983)
1983)
1983)
1983)
1983)
1983)
1983)
1983)
1983)
1983)
1984)
1984)
1984)
1992)
2093)
*
Note:
Table 15. 24h Panel Data Regression Results Equation 1 – Treatment Group
67
p<0.1; **p<0.05; ***p<0.01
Master Thesis – Navid Sadat-Razavi
Panel Data Regression Results – Control Group
Dependent variable: Willingness to use electrical energy (usage/price)
Persons
Building
Age
Building
Size
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
-0.15
-0.08
-0.14
-0.19*
-0.15
-0.10
0.14**
0.13
0.21**
0.23*
0.41***
0.37***
0.39***
0.26**
0.20
0.41***
0.54***
0.52***
0.54***
0.59***
0.27**
0.15
-0.09
-0.19
(0.11)
(0.09)
(0.10)
(0.11)
(0.10)
(0.07)
(0.07)
(0.08)
(0.10)
(0.12)
(0.11)
(0.11)
(0.11)
(0.12)
(0.12)
(0.11)
(0.12)
(0.11)
(0.15)
(0.15)
(0.11)
(0.09)
(0.12)
(0.13)
*
**
-0.03
0.14
0.13
0.01
0.01
0.06
0.06
0.05
-0.09
-0.08
-0.10
-0.06
-0.17
-0.10
-0.11
0.04
0.02
-0.02
-0.22
(0.10)
(0.08)
(0.09)
(0.10)
(0.09)
(0.07)
(0.06)
(0.08)
(0.10)
(0.11)
(0.11)
(0.10)
(0.10)
(0.11)
(0.11)
(0.10)
(0.11)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
0.02
(0.001)
Building
Type
0.18
**
(0.09)
Roof
Insulation
0.02
(0.001)
0.02
(0.001)
0.02
(0.001)
0.07
0.08
0.11
(0.07)
(0.08)
(0.09)
0.02
(0.001)
0.16
**
(0.08)
0.02
(0.001)
0.12
**
(0.06)
***
-0.16
-0.18
-0.16
-0.14
-0.35
-0.60
(0.29)
(0.24)
(0.26)
(0.30)
(0.26)
(0.19)
0.02
(0.001)
0.14
**
(0.05)
-0.45
**
(0.18)
Solar
Influx
Constant
Observatio
ns
R
2
Adjusted
R2
Hausman
(p>chi2)
F Statistic
0.02
(0.001)
0.23
***
(0.07)
-0.75
***
(0.23)
-0.001
**
0.02
(0.001)
0.16
*
(0.09)
-0.98
***
(0.28)
-0.001
**
0.03
(0.001)
0.17
*
***
(0.32)
-0.001
(0.001)
0.19
(0.09)
-1.02
0.02
*
**
(0.09)
-1.09
***
(0.31)
-0.002
**
0.02
(0.001)
0.15
*
***
(0.29)
-0.002
(0.001)
0.19
(0.09)
-0.96
0.02
*
**
(0.09)
-0.99
***
(0.30)
-0.001
**
0.02
(0.001)
0.27
***
(0.10)
-1.22
***
(0.33)
-0.002
0.02
(0.001)
0.24
**
(0.10)
-1.15
***
(0.33)
0.02
(0.001)
0.23
***
(0.09)
-1.32
***
(0.30)
**
0.0000
-0.003
**
0.02
(0.001)
0.17
*
-0.36
***
-0.15
-0.06
-0.15
-0.22
(0.10)
(0.14)
(0.14)
(0.11)
(0.09)
(0.11)
(0.12)
***
***
***
***
***
***
0.02***
(0.001)
(0.001)
0.02
0.01
0.02
0.02
(0.001)
(0.002)
(0.002)
(0.001)
***
***
***
***
0.28
0.44
0.38
0.33
0.02
(0.001)
0.18
**
0.02
0.22
**
0.22**
(0.10)
(0.09)
(0.12)
(0.12)
(0.09)
(0.08)
(0.10)
(0.10)
-0.29
0.28
0.12
0.22
0.06
0.19
-0.08
-0.12
(0.32)
(0.30)
(0.40)
(0.39)
(0.31)
(0.25)
(0.32)
(0.34)
**
**
-0.01
-0.03
0.09
(0.004)
(0.002)
(0.002)
(0.002)
(0.001)
(0.002)
(0.002)
(0.003)
(0.004)
(0.01)
(0.02)
(0.34)
-1.16
-1.33*
-1.71**
-2.08**
-1.40**
-0.96*
-0.70
-0.46
1.04
0.58
0.74
1.11
1.15
1.93
1.91
1.01
0.13
-1.06
-2.82***
-2.55**
0.66
0.31
-0.43
-0.85
(0.87)
(0.76)
(0.79)
(0.90)
(0.71)
(0.55)
(0.64)
(0.75)
(0.87)
(0.90)
(0.93)
(0.86)
(0.95)
(1.19)
(1.24)
(1.09)
(1.00)
(1.08)
(1.04)
(1.10)
(0.97)
(0.84)
(1.06)
(1.03)
1,020
986
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
1,020
0.18
0.26
0.24
0.19
0.20
0.37
0.46
0.41
0.32
0.31
0.31
0.30
0.33
0.28
0.26
0.27
0.25
0.22
0.14
0.16
0.20
0.22
0.16
0.14
0.18
0.25
0.24
0.19
0.20
0.37
0.46
0.41
0.32
0.31
0.31
0.30
0.33
0.28
0.26
0.26
0.25
0.22
0.13
0.16
0.19
0.21
0.16
0.14
0.1
0.1
0.2
0.2
0.1
0.1
0.2
0.2
0.2
0.1
0.1
0.1
0.2
0.2
0.2
0.1
0.1
0.2
0.1
0.1
0.1
0.1
0.1
0.1
38.18***( 56.04***( 53.64***( 39.62***( 42.25***( 99.79***( 143.90***( 99.79***( 69.06***( 64.72***( 65.55***( 61.76***( 70.83***( 55.69***( 51.40***( 52.34***( 49.13***( 41.90***( 22.66***( 31.83***( 41.12***( 46.45***( 32.78***( 28.13***(
df = 6;
df = 6;
df = 6;
df = 6;
df = 6;
df = 6;
df = 6;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 7;
df = 6;
df = 6;
df = 6;
df = 6;
df = 6;
1013)
979)
1013)
1013)
1013)
1013)
1013)
1012)
1012)
1012)
1012)
1012)
1012)
1012)
1012)
1012)
1012)
1012)
1012)
1013)
1013)
1013)
1013)
1013)
*
Note:
Table 16. 24h Panel Data Regression Results Equation 1 – Control Group
68
p<0.1; **p<0.05; ***p<0.01
Master Thesis – Navid Sadat-Razavi
Standard deviation
Proportion of Variance
Cumulative Proportion
Building Age
Building Size
Building Type
Solar Panels
Terrain Type
Persons
Solar Heating
Ventilation Type
Roof Insulation
PC1
PC2
PC3
PC4
PC5
PC6
PC7
PC8
PC9
1,74
0,33
0,33
0,29
-0,56
0,28
-0,03
0,45
0,42
-0,04
0,27
0,25
1,22
0,17
0,50
-0,19
0,15
-0,53
-0,24
-0,04
0,39
-0,05
0,15
-0,16
1,10
0,13
0,64
-0,43
0,05
0,11
0,37
-0,04
0,04
-0,73
0,09
0,33
1,00
0,11
0,75
-0,17
0,09
0,25
-0,80
0,29
-0,22
-0,35
-0,06
-0,11
0,96
0,10
0,85
-0,47
-0,07
0,43
0,25
0,16
0,15
0,17
0,08
-0,67
0,84
0,08
0,93
-0,53
0,01
0,23
-0,24
-0,22
0,19
0,49
-0,01
0,54
0,63
0,04
0,97
0,27
-0,12
0,39
-0,18
-0,77
-0,22
-0,21
0,12
-0,18
0,51
0,03
1,00
-0,04
-0,06
-0,21
-0,09
-0,04
-0,68
-0,14
-0,67
-0,10
0,00
0,00
1,00
0,29
0,79
0,38
0,10
0,20
0,24
0,05
0,14
0,11
Table 17. PCA Analysis of All Available Household Variables
69
Master Thesis – Navid Sadat-Razavi
7.1 Qurrent Energie Dashboard Screenshots
70