Russell G. Worden
Managing Director, State Regulatory Operations
January 13, 2017
ADVICE 3463-E-A
(U 338-E)
PUBLIC UTILITIES COMMISSION OF THE STATE OF CALIFORNIA
ENERGY DIVISION
Supplemental Filing to Advice 3463-E: Submission of High
Opportunity Projects and Programs Proposal – Comprehensive
Value Chain Heating, Ventilation, and Air Conditioning
Program
SUBJECT:
PURPOSE
The purpose of this Supplemental Advice Letter (AL) is to modify SCE's program
proposal in AL 3463-E, based on recommendations and questions from Energy Division
Staff, as a result of Staff’s initial review of SCE's proposal contained in AL 3463-E,
originally submitted on August 31, 2016.
This advice filing supplements in part and will not change the substance of the original
Advice 3463-E.
BACKGROUND
On October 8, 2015, the legislature enacted Assembly Bill (AB) 802, which amended
Section 381.2 of the California Public Utilities Code. New subsection (b) requires the
Commission to authorize, by September 2016, electrical corporations or gas
corporations to provide financial incentives, among other things, to increase the energy
efficiency (EE) of existing buildings based on the reduction of metered energy
consumption as a measure of energy savings. New subsection (c) states that “[e]ffective
January 1, 2016, electrical corporations and gas corporations are authorized to
implement the provisions of subdivision (b) for high opportunity projects or programs.”
The High Opportunity Projects or Programs (HOPPs) efforts is intended to identify “high
opportunity” EE interventions in line with the legislative direction before the Commission
adopts a comprehensive program to provide incentives to improve the EE of existing
buildings.
P.O. Box 800
8631 Rush Street
Rosemead, California 91770
(626) 302-4177
Fax (626) 302-6396
ADVICE 3463-E-A
(U 338-E)
-2-
January 13, 2017
On October 30, 2015, the assigned Commissioner and Administrative Law Judge (ALJ)
issued a scoping memorandum regarding EE “Rolling Portfolios” and established a
process specifically for addressing HOPPs, along with other aspects of AB 802.1
The December 30, 2015 ACR provides the following minimum standards for the
development and implementation of HOPPs.2 HOPPs may be funded from unspent
funds in existing programs. There are no minimum requirement for expected savings for
HOPPs. HOPPs may feature a variety of incentive structures, so long as the payment
strategy reflects an accurate valuation of the savings. All HOPPs must incorporate a
measurement and verification (M&V) plan, including the M&V protocols set out in the
ACR. A key feature is that HOPPs proposals should emphasize measurement of the
effects of interventions as detailed in Attachment A of the ACR.
The ACR allows PAs to submit HOPPs with the documentation and specifications listed
in the ACR. HOPPs are to be submitted through the CPUC Energy Division’s existing
Custom Measure and Project Archive (CMPA) system.
On August 31, 2016, SCE filed AL 3463-E proposing the Comprehensive Value Chain
Heating, Ventilation, and Air Conditioning (HVAC) Program as a HOPP. The
Commission's Energy Division staff provided SCE with questions and
recommendations, which are addressed in this supplemental filing.
MODIFICATIONS TO AL 3463-E
SCE’s modified Comprehensive Value Chain HVAC HOPP proposal is included in the
Revised Attachment A of this AL. Among others changes, SCE modified the following
aspects of its Comprehensive Value Chain HVAC HOPP proposal, in response to
Energy Division staff’s recommendations:
1. Incentive structure was simplified from a three tier to a two tier structure (i.e.,
standard versus premium);
2. Incentives payments changed from a three stage payment (30 percent/20
percent/50 percent) to a two stage payment (20 percent/80 percent);
3. Budget forecast increased due to additional developmental funding during the
first year; and
4. Created a maximum baseline EER efficiency of EER 7 to avoid incentivizing
lower opportunity projects
Appendix A of Attachment A includes a summary table listing Energy Division Staff’s
questions and recommendations, with the corresponding response from SCE and/or a
reference to where the recommendations were addressed in the proposal.
1
2
“Assigned Commissioner and Administrative Law Judge's Ruling and Amended Scoping
Memorandum Regarding Implementation of Energy Efficiency ‘Rolling Portfolios’ (Phases IIB
and IIIA of R.13-11-005)” (Phase IIB/IIIA scoping memo).
ACR, Paragraph 5.
ADVICE 3463-E-A
(U 338-E)
-3-
January 13, 2017
TIER DESIGNATION
Pursuant to General Order (GO) 96-B, Energy Industry Rule 5.1, and the December 30,
2015 ALJ Ruling Ordering Paragraph 2, this advice letter is submitted with a Tier 1
designation.
EFFECTIVE DATE
This supplemental advice filing will become effective on the same day as the original
filing, Advice 3463-E, which is September 30, 2016.
PROTESTS
SCE asks that the Commission, pursuant to GO 96-B, General Rules 7.5.1, maintain
the original protest and comment period designated in Advice 3463-E and not reopen
the protest period . The modifications included in this supplemental advice filing do not
make substantive changes that would affect the overall evaluation of the filing.
NOTICE
In accordance with General Rule 4 of GO 96-B, SCE is serving copies of this advice
filing to the interested parties shown on the attached GO 96-B and R.13-11-005 service
lists. Address change requests to the GO 96-B service list should be directed by
electronic mail to [email protected] or at (626) 302-4039. For changes to
all other service lists, please contact the Commission’s Process Office at (415)
703-2021 or by electronic mail at [email protected].
Further, in accordance with Public Utilities Code Section 491, notice to the public is
hereby given by filing and keeping the advice filing at SCE’s corporate headquarters.
To view other SCE advice letters filed with the Commission, log on to SCE’s web site at
https://www.sce.com/wps/portal/home/regulatory/advice-letters.
For questions, please contact Eric Yamashita at (626) 302-7306 or by electronic mail at
[email protected]
Southern California Edison Company
/s/ Russell G. Worden
Russell G. Worden
RGW:ey:jm
Enclosures
CALIFORNIA PUBLIC UTILITIES COMMISSION
ADVICE LETTER FILING SUMMARY
ENERGY UTILITY
MUST BE COMPLETED BY UTILITY (Attach additional pages as needed)
Company name/CPUC Utility No.: Southern California Edison Company (U 338-E)
Utility type:
Contact Person: Darrah Morgan
 ELC
 GAS
 PLC
 HEAT
Phone #: (626) 302-2086
 WATER
E-mail: [email protected]
E-mail Disposition Notice to: [email protected]
EXPLANATION OF UTILITY TYPE
ELC = Electric
PLC = Pipeline
GAS = Gas
HEAT = Heat
(Date Filed/ Received Stamp by CPUC)
WATER = Water
Advice Letter (AL) #:
3463-E-A
Tier Designation:
1
Supplemental Filing to Advice 3463-E, Submission of High Opportunity Projects and Programs
Subject of AL:
Proposal- Comprehensive Value Chain Heating, Ventilation, and Air Conditioning Program
Keywords (choose from CPUC listing):
Compliance
AL filing type:  Monthly  Quarterly  Annual  One-Time  Other
If AL filed in compliance with a Commission order, indicate relevant Decision/Resolution #:
Does AL replace a withdrawn or rejected AL? If so, identify the prior AL:
Summarize differences between the AL and the prior withdrawn or rejected AL:
Confidential treatment requested?  Yes  No
If yes, specification of confidential information:
Confidential information will be made available to appropriate parties who execute a nondisclosure agreement.
Name and contact information to request nondisclosure agreement/access to confidential information:
Resolution Required?  Yes  No
Requested effective date:
9/30/16
No. of tariff sheets:
-0-
Estimated system annual revenue effect: (%):
Estimated system average rate effect (%):
When rates are affected by AL, include attachment in AL showing average rate effects on customer classes
(residential, small commercial, large C/I, agricultural, lighting).
Tariff schedules affected:
None
Service affected and changes proposed1:
Pending advice letters that revise the same tariff sheets:
1
Discuss in AL if more space is needed.
None
All other correspondence regarding this AL shall be sent to:
CPUC, Energy Division
Attention: Tariff Unit
505 Van Ness Avenue
San Francisco, California 94102
E-mail: [email protected]
Russell G. Worden
Managing Director, State Regulatory Operations
Southern California Edison Company
8631 Rush Street
Rosemead, California 91770
Telephone: (626) 302-4177
Facsimile: (626) 302-6396
E-mail: [email protected]
Laura Genao
Managing Director, State Regulatory Affairs
c/o Karyn Gansecki
Southern California Edison Company
601 Van Ness Avenue, Suite 2030
San Francisco, California 94102
Facsimile: (415) 929-5544
E-mail: [email protected]
Attachment A
High-Opportunity Projects and Programs (HOPPs) Proposal:
Comprehensive Value Chain HVAC (CVC-HVAC)
1
Table of Contents
Table of Contents .......................................................................................................................... 2
1.
Overview ............................................................................................................................ 4
1.1
Program Overview ........................................................................................................... 5
2.
HOPPs Principles and Program Rationale ......................................................................... 6
3.
General Description and Current Program Offerings ......................................................... 6
3.1
Unit Replacement ............................................................................................................ 7
3.2
Duct Renovation .............................................................................................................. 7
3.3
System Maintenance ....................................................................................................... 8
4.
Program Drivers ................................................................................................................. 9
4.1
HVAC Stranded Potential ................................................................................................ 9
4.2
HVAC Gross Realization Rates ..................................................................................... 11
4.3
Peak Period Cost of HVAC ............................................................................................ 15
5.
Risk Assessment and Intervention Strategies .................................................................. 16
6.
Program Design ............................................................................................................... 18
6.1
Program Structure ......................................................................................................... 18
6.1.1
Program Eligibility Requirement #1 (Whole System Upgrade) ............................... 19
6.1.2
Program Eligibility Requirement #2 (Minimum Baseline System Efficiency) .......... 19
6.1.3
Program Eligibility Requirement #3 (Non-operational System Replacement) ........ 19
6.2
HVAC System Upgrade Lite .......................................................................................... 20
6.2.1
6.3
Claimed/Reported Energy Savings ............................................................................... 20
6.3.1
Quality Assurance and Quality Control (QA/QC) ................................................... 21
6.3.2
Continuous Intervention, Equipment Testing, and Up-Sales .................................. 21
6.3.3
Gas Savings Claims ............................................................................................... 21
6.3.4
Program List of Measures ...................................................................................... 21
6.4
Incentive Design ............................................................................................................ 22
6.4.1
Customer Incentive Structure ................................................................................. 23
6.4.2
Contractor Incentive Structure ................................................................................ 24
7.
Program Savings Potential and Program Objectives ....................................................... 25
7.1
8.
Additional Eligibility Requirement #1 (HVAC System Upgrade Lite) ...................... 20
Enhanced Measure Extended Useful Life (EUL) ........................................................... 27
Savings Calculation General Method ............................................................................... 29
2
9.
Data Collection Strategy .................................................................................................. 29
9.1
Baseline & Post-Installation Data Collection ................................................................. 29
9.2
Independent Variables ................................................................................................... 29
9.3
Static Factors ................................................................................................................. 30
9.4
Load............................................................................................................................... 30
9.5
Operating Conditions ..................................................................................................... 30
9.6
Building Envelope .......................................................................................................... 30
9.7
Reporting Period ............................................................................................................ 30
10.
Field Data Collection ........................................................................................................ 30
10.1
Basic System Parameters and Calculations .................................................................. 32
10.2
Capacity Measurements ................................................................................................ 33
10.3
Wattage Measurements ................................................................................................ 33
10.4
Measurement Conditions ............................................................................................... 34
10.5
Data Requirements ........................................................................................................ 34
10.6
Energy Efficiency Calculations Background .................................................................. 35
10.7
Calculations, Regression Model, and Normalization ..................................................... 35
11.
Modeling Approach and Specification .............................................................................. 35
11.1
First Stage: Binning Process ......................................................................................... 36
11.2
Second Stage: Random Coefficients Model .................................................................. 38
11.3
Third Stage: Savings Estimation .................................................................................... 39
12.
Threshold for Expected Savings ...................................................................................... 40
13.
Baseline Adjustments ....................................................................................................... 40
13.1
Non-Routine Adjustments .............................................................................................. 40
14.
Net-to-Gross Adjustment for Net Energy Savings ............................................................ 41
15.
Quasi-Experimental Design for Estimating Savings ......................................................... 41
Appendices ................................................................................................................................. 47
15.1 Appendix A: SCE Response to Review Sheet for 2016 HOPPs Proposal – Advice Letter
3463-E ...................................................................................................................................... 48
15.2
Appendix B: Estimated Uncertainty of Field-Measured HVAC System Efficiency ......... 55
15.3
Appendix C: Random Coefficients Model for AB802 ..................................................... 63
3
Overview
Southern California Edison (SCE) respectfully submits this proposal for its Comprehensive Value
Chai n HVAC (CVC-HVAC) HOPPs, which is designed to leverage smart meter investments and
Normalized Metered Energy Consumption (NMEC) energy savings approaches to optimize HVAC
systems in non-residential buildings. The program will support the NMEC approach outlined in
the July 19, 2016, Proposed Decision in California Public Utilities Commission (CPUC) Rulemaking
13-11-005; requirements provided in the December 30, 2015, Ruling regarding HOPPs; and the
wider climate objectives outlined by the Legislature and Governor’s Office in bills such as AB 32,
SB350, and AB802.
CVC-HVAC is a comprehensive strategy for non-residential customers. It creates linkages
between existing programs to offer a complete, value-chain proposition to end-use customers.
The existing programs are:
•
•
•
Early Retirement (ER)/Commercial Upstream (CUS).
Commercial Quality Installation (CQI).
Commercial Quality Maintenance (CQM).
SCE’s CVC-HVAC program incorporates Roof Top Unit (RTU) replacement with high-efficiency
units, air delivery upgrades and renovations, and comprehensive maintenance strategies, to
ensure the deepest energy savings. The program is designed to capture stranded energy savings,
achieved at a system level from non-prescribed measures that currently cannot be captured in a
work-paper approach to savings claims. It aims to credit site-specific conventional and nonconventional system optimization activities that result in demand savings.
Using information-based monitoring while leveraging a pay-for-performance model will expand
market participation in whole system upgrades, and leverage contractor outreach to organically
upsell incremental energy efficiency measures to end-use customers. Table 1 summarizes the
key elements of the CVC-HVAC program design.
4
Program Overview
Table 1 - CVC-HVAC Program Design Summary
Scope
Program Matrix
Program Delivery
Participation Incentive
Recipient
Performance Incentive
Recipient
Effective Useful Life
(EUL)
Measure Installation
Type
-
Rooftop Unit (RTU) Replacement
Duct Renovation
ASHRAE Standard 180 Maintenance Agreement
Unit Replacement
Duct Renovation
Contractor
Contractor
Maintenance Agreement
N/A
Customer
Contractor
N/A
18
8
N/A
Early Retirement (RET)
Retrofit Add-On (REA)
N/A
Two verification methods will be deployed to annualize energy savings ex post
Verification Method
-
Additional Comments
Option 1: Unit Replacement  Duct Renovation  3-Year Maintenance Agreement
Option 2: Duct Renovation  3-Year Maintenance Agreement
Primary: Whole-Building aggregate analysis/Normalize Meter Energy Consumption
(NMEC)
- Secondary: Instantaneous field data collection
Customers may select to participate via a single program path from two options below:
The program design elements found in Table 1, along with many other elements, are discussed
in greater detail throughout this proposal.
5
HOPPs Principles and Program Rationale
The proposed HOPPs program is developed in response to Assembly Bill 802, Section 6,
Subsection 381.2. Subdivision C1. A goal from the outset was to address the observed variability
and high potential of energy savings on unrealized rooftop unit (RTU) HVAC savings. From the
Impact Evaluation Study of 2013-14 Upstream HVAC Programs, the gross realization rate (GRR)
of unit replacement measures is 71.2%. However, considering only units above 10 tons, the GRR
significantly drops to below 20%2. The CVC-HVAC program has the potential to positively impact
the GRR for all units by leveraging the performance-based reporting model, and enhancing
overall energy savings and program performance.
Three areas of activity were analyzed to inform principles linked to SCE’s CVC-HVAC
development:
1. Programs and projects that quantify a change in energy consumption at the meter, using
existing conditions as the baseline, subject to adjustments (normalization) for weather,
occupancy, equipment standards (excluding Title 24), etc.
2. Market transformation programs with an appropriate level of funding to achieve deeper
energy efficiency savings.
3. Pay-for-performance programs linking incentive payments to actual achieved energy savings.
General Description and Current Program Offerings
The following sub-sections provide a brief summary of the current SCE program offerings that
make up the CVC-HVAC proposal. Existing program models are designed to operate
independently, and while inter-program project participation is a practice for some customers
and contractors, the industry practice has generally approached non-residential HVAC using a
piecemeal approach.
1
“Effective January 1, 2016, electrical corporations and gas corporations are authorized to implement the
provisions of subdivision (b) for high opportunity projects or programs. The commission shall provide expedited
authorization of high opportunity projects and programs to apply the savings baseline provisions in subdivision (b)”
2
DNV-GL. Impact Evaluation of 2013-14 Upstream HVAC Programs (HVAC1): CALMAC Study ID CPU0116
6
Unit Replacement
HVAC energy consumption accounts for a large fraction of businesses, and is directly related to
the operational efficiency of the RTU, which has most, if not all, of the energy-consuming
components for unitary and split-package unit systems. These include one or more compressors,
the supply fan motor, and the return fan motor. Upgrading the RTU to a system with more
efficient fan motors and compressor(s) will decrease the power per unit of cooling (kW/ton), and
overall annual energy consumption per unit of cooling (kWh/ton-year). Unit replacement scope
must meet or exceed standing California Title 24 Standards and Regulations at the time of
project application.
Figure 1 - RTU Replacement Scope
Duct Renovation
RTU replacement is a well-known business owner strategy for optimizing and reducing energy
consumption. However, with a poor delivery system, only a fraction of the conditioned air will
reach end users. As a result, the time to reach the set point temperature extends, increasing the
overall unit runtime. Increased runtime results in more energy consumption. This is especially
true during summer peak periods, when hot, unconditioned air from outdoors or plenums
penetrates cool airstreams.
Commercial Quality Installation (CQI) attempts to improve delivery systems through three
primary activities:
1. Designing and sizing ductwork properly, to ensure even air distribution zone end uses.
2. Addressing duct leakage to minimize conditioned air loss.
3. Renovating duct insulation to minimize heat gain.
7
Figure 2 - Duct Renovation and Optimization Scope
System Maintenance
After installing a new RTU, or ductwork optimization or renovation, equipment and hardware
maintenance is promoted through the CQM program. This program is leveraged when
customers enter into maintenance agreements with contractors. When the maintenance
agreement is initiated, equipment efficiencies are returned to original design performance levels
and brought up to Title 24 minimum code by applying diagnostic methods and the detailed
HVAC inspection and maintenance tasks of American National Standards Institute
(ANSI)/American Society of Heating, Refrigerating and Air Conditioning (ASHRAE)/Air
Conditioning Contractors of America (ACCA) Standard 180. The contractor will then continue to
maintain the equipment on a quarterly basis, and perform mandatory tasks as defined in the
existing CQM Program Implementation Plan (PIP).
Figure 3 - Maintenance Agreement Scope
8
Program Drivers
SCE’s HOPPs program promotes a comprehensive, value-chain-driven approach to package unit
replacement, optimization, and ongoing peak performance. Customers and contractors stand to
maximize system performance and quickly identify equipment malfunctions due to a mandatory
three-year system maintenance agreement. Mandatory HOPPs maintenance requirements are
leveraged, to help drive persistence of overall systems savings.
HVAC Stranded Potential
As a part of the Navigant AB 802 Technical Analysis for Potential Stranded Savings3, the most
prominent commercial measures for additional potential include HVAC measures that will be
targeted through the comprehensive and holistic manner described in SCE’s HOPPs application.
3
Navigant. AB 802 Technical Analysis Potential Stranded Savings
<http://www.cpuc.ca.gov/WorkArea/DownloadAsset.aspx?id=11189>
9
As Figure 4 and Figure 5 illustrate, HVAC (grey) represents one of the most prominent and
relevant opportunities related to addressing stranded savings and Behavioral, Operational, and
Retro-Commissioning (BROs) elements:
Figure 4 - Stranded Potential by End Use (GWh)
Figure 5 - Stranded Potential by End Use (MW)
Additionally, a study performed in 2014 concluded that “it does not appear California is on-track
to meet the HVAC Action Plan goal of a 50 percent permit rate by 2015, and it is even less likely
of meeting or exceeding the 90 percent goal by 2020 without consistent intervention”4. In
developing a CVC-HVAC proposal, SCE wishes to explore an integrated-systems approach to non-
4
DNV-GL. HVAC Permitting: A Study to Inform IOU HVAC Programs: CALMAC Study ID PGE0349.01
10
residential HVAC, to drive building design compliance through its program participant screening
while capturing stranded potential in the prevailing program design.
The program is designed to capture stranded energy savings achieved at a system level from
non-prescribed measures that currently cannot be captured in a work paper approach to savings
claims. It aims to credit both conventional and non-conventional system optimization “site
specific” activities that result in demand savings. Additionally, this program applies a more
cohesive strategy that aligns contractor incentives to continuously engage customers to capture
behavioral and operational savings, and sell additional energy efficiency or management
products.
HVAC Gross Realization Rates
According to the Impact Evaluation of 2013-14 Upstream HVAC Programs (HVAC1)5 published in
2016, the Gross Realization Rate (GRR) for all unitary system replacement MWh and MW are
71.2% and 128.9%, respectively. These findings are illustrated in Table 2 and Table 3, and show
the 2013-14 GWh and MW ex-ante and ex-post data. These figures show the ex-ante claims are
relatively representative.
5
DNV-GL. Impact Evaluation of 2013-14 Upstream HVAC Programs (HVAC1): CALMAC Study ID CPU0116
11
Table 2 - 2013 - 2014 kWh Savings GRR for All Unitary Systems
Table 3 - 2013 - 2014 kW Savings GRR for All Unitary Systems
However, while the weighted average data would imply a relatively stout GRR, further analysis
shows differing performance results between “Large” units (>20 ton) and “Small” units (< 20
ton). Isolating “Large” unitary systems, the GRR falls to 17.2% and 149.7% for MWh and MW,
respectively. Tables 4 through 7 illustrate a more detailed breakdown of 2013-14 MWh and MW
ex-ante and ex-post representation of unit replacement data.
12
Table 4 - 2013 - 2014 kWh Savings GRR for Small Unitary Systems
13
Table 5 - 2013 - 2014 kW Savings GRR for Small Unitary Systems
Table 6 - 2013 - 2014 kWh Savings GRR for Large Unitary Systems
Table 7 - 2013 - 2014 kW Savings GRR for Large Unitary Systems
14
The GRR for the existing HVAC programs are relatively low, or have high uncertainty. The
allowable and prescribed energy savings per measure, in tandem with the incentive offerings in
current programs, have demonstrated a non-cost-effective strategy. By using a meter-based
approach, final energy savings and incentives are credited based on realized energy savings.
Furthermore, the allowable and prescribed energy savings per measure, in tandem with the
minimum incentive levels required to influence customer participation, create a non-costeffective strategy.
Peak Period Cost of HVAC
According to the Department of Energy Buildings Data Book6 and the 2006 California End-Use
Survey7, the total HVAC energy contribution for small and large commercial businesses typically
ranges anywhere from 10-20% of overall building consumption. However, HVAC energy
consumption is greatest during peak hours and summer months, and reportedly contributes
about 40% of the overall building consumption during these periods.
The peak periods associated with A/C usage also align with the highest electricity prices charged
under mandatory Time-of-Use (TOU) rate plans for SCE’s business customers (D.13-03-031). As
depicted in Figure 6, the highest costs associated with TOU energy prices occur on summer
weekdays, between noon and 6:00 p.m.. Because HVAC load shape tends to align as cleanly as it
does with these new peak TOU rates, SCE anticipates the overlap will create a strong value
proposition tied to potential energy savings and time-related demand that integrated programs
like CVC-HVAC can help address.
Figure 6 - Typical SCE TOU Rate Schedule
6
U.S. Department of Energy Buildings. Energy Data Book <http://buildingsdatabook.eren.doe.gov/CBECS.aspx>
Itron. 2006 California Commercial End-use Survey <http://www.energy.ca.gov/2006publications/CEC-400-2006005/CEC-400-2006-005.PDF>
7
15
Risk Assessment and Intervention Strategies
Table 8 catalogues the identified risks (or barriers) involved with implementing a CVC-HVAC
program. This table is a basis for the risk management plan development that would accompany
the launch of this HOPPs program:
Table 8 - Risk Assessment
Potential Risks
Intervention Strategies
Lack of value proposition awareness.
Research has not been able to effectively quantify the full
energy and operational benefits of duct renovation and
quality maintenance agreements.
Lack of value proposition awareness.
End-use customers are not educated on the system
impacts and implications on energy consumption of an
HVAC system if the unit and distribution network is not
installed and maintained using industry best practice.
16
The CVC-HVAC program will continue to implement
the unit replacement scope to penetrate the nonresidential HVAC end-use. This existing program
offering serves as the point of entry to quickly increase
exposure to the comprehensive program and utilize
contractors as the communication engine to drive the
non-residential HVAC market evolution. The
contractor will be motivated by elevated incentives that
are dependent on system performance over the course
of one year. As such, with potential revenue under
consideration, contractors are expected to market the
importance of system efficiency activities and
subsequent up-sales more aggressively.
Potential Risks
Intervention Strategies
Availability of higher-efficiency equipment.
There is a general tendency from end-use customers to
purchase equipment with the lowest up-front cost,
irrespective of the increased operations and maintenance
costs over time. As a result of the low product demand,
stocking and marketing patterns from distributors or
vendors trend toward low-price, standard-efficiency
systems. One result is a lack of on-hand units and
subsequent back-order for higher-performing equipment.
Risk Register
Greater incentives for higher efficiency equipment and
its proper installation should increase availability of
equipment and could reduce the average cost. The
decreased cost may positively affect the market supply.
Risk Intervention Strategies
Limited resources for consumers to select qualified
contractors.
There are select contractors that are currently capable of
offering a comprehensive HVAC system overhaul.
However, there are few resources that advise consumers
of service providers that are able to perform this scope of
work as a result of the existing fragmented nonresidential HVAC program.
This proposal aims to include a program element that
incorporates training, marketing, and incentives to help
contractors understand, implement, and effectively
communicate the value of HVAC quality maintenance
and energy efficiency. Communication and incentive
being drivers for success, the contractors are expected
to market their expertise to perform an all-inclusive
HVAC overhaul strategy.
Commoditized business model practices.
The CVC-HVAC program results are regulated by
Service Agreements between customers and
contractors, establishing an on-going relationship of
trust that also then enables better decisions to be made
about replacement of equipment with high-efficiency
systems and the proper quality installation of those
systems.
It is logical to assume that the HVAC industry would
strive to take the necessary education and training to
deliver high quality service. However, market dynamics
have not supported such logic, where the industry has
largely become commoditized and the lowest bid will
typically be awarded a contract in spite of a sacrifice to
quality.
Organizational customs.
The resulting increase in market share of highefficiency equipment and quality installation and
maintenance services should allow for increased levels
of customer, installer, and distributor/manufacturer
knowledge and interest in these systems. This in turn
should make it easier to achieve further increases in the
market share of these energy saving practices.
The HVAC industry has largely become commoditized
into an industry driven by low costs and quality where
quality is assumed but not understood or valued by the
customer. This is a result, in part, of contractors having
minimal success in communicating the value of QI/QM
to consumers, and consumers not understanding the
linkages between comfort and energy use.
17
Program Design
Program Structure
The Comprehensive Value Chain HVAC System Upgrade includes unit replacement, duct design
and renovation, and a three-year maintenance agreement. The program is designed to enable
commercial customers to participate in the CVC-HVAC program, if they are planning to engage in
equipment replacement, renovation, or optimization across HVAC systems, as described in
Figure 7.
Figure 7 - Program Design Overview
Unit
Replacement
Duct
Renovation
•Activity: Replace existing rooftop unit.
•Value Add: Baseload offering and customer penetration. Paired with duct distribution system, equipment
can be optimized and/or right-sized using the appropriate system delivery.
•Activity: Duct renovation and optimization .
•Value Add: Supports energy savings from unit replacement by maximizing replaced unit system cooling
delivery to end-use zones.
•Activity: 3-year ASHRAE Standard 180 quarterly maintenance agreement.
•Value Add: Promotes persistence of energy savings from unit replacement and duct renovation, continuous
Maintenance program intervention to repair or optimize existing equipment, and serves as an engine for field data
collection.
Agreement
Per Title 24 Standards and Regulations, Section 124, existing ductwork must undergo testing to
resolve any deficiencies in design, leakage, or inadequate insulation, historically rendering such
work ineligible for savings due to code triggering. Because AB 802 authorizes meter-based
savings approaches that allow below- and to- code savings claims, energy-saving activities
(upgrading and repairing outdated or poorly-maintained ductwork) become eligible for
incentives. Even further, the amount of energy saved for duct renovation has historically been
difficult (if not impossible) to quantify, due to the varying conditions of duct systems. A meterbased savings approach is a natural resolution to capture and quantify savings from duct
renovation that would have otherwise been unclaimed.
When the installation and renovation scope(s) of work are completed, the customer will be
required to enter a three-year maintenance agreement with the same contractor. This
contractor must be certified to perform all required duties of the existing maintenance program,
18
as well as to perform system Engineering Evaluation Request (EER) testing at each quarterly visit.
The maintenance plan is used primarily to ensure energy savings persistence from unit
replacement and duct renovation, and to act as the driver for continuous intervention. The
program may require up to one EER data collection set on a quarterly basis, to be used in the
normalized meter data analysis. If the contractor fails to execute the three-year maintenance
agreement, or if energy savings do not materialize, 100% of any incentive awarded to the
contractor will be clawed back.
An integral component of the CVC-HVAC program is the mandatory maintenance agreement. A
customer is not eligible for program incentives unless the contractor and customer enter a
three-year maintenance agreement that satisfies program reporting requirements. For the first
year of program operation, 100% of participants will be flagged for a study to broaden the data
set and increase modeling accuracy. The study sample size and number of maintenance visits for
contractors may be reconsidered, based on the success or shortcomings of the preliminary study
group.
6.1.1 Program Eligibility Requirement #1 (Whole System Upgrade)
During the project application and customer survey, all units and meters at the identified site
will be included as a reporting requirement for program eligibility. The grand total energy
consumption across all meters at a specific site will be used as the point of reference for
evaluation.
6.1.2 Program Eligibility Requirement #2 (Minimum Baseline System Efficiency)
To encourage contractors and customers to target systems with the greatest energy savings
potential, at the program outset, project applications will be limited to units that have a baseline
(test-in) EER not greater than or equal to 7 (EER < 7). For individual units where EER is greater
than or equal to 7 (EER ≥7), the unit will NOT be considered a high opportunity, and will not be
eligible for program participation.
Exception: For whole-system renovations (the program definition will be “systems with
at least three RTUs”), if the AVERAGE baseline (test-in) EER is not greater than or equal to
7 (EER < 7), the system as a whole is then eligible for program participation.
6.1.3 Program Eligibility Requirement #3 (Non-operational System Replacement)
For cases in which the site currently has a non-operational system or unit, the unit is no longer
considered eligible for program incentives. The customer may replace the non-operational unit
and perform a system test-in, to baseline the distribution system efficiency. The customer is
eligible for energy savings associated with duct distribution system renovation and optimization.
19
However, energy savings as a result of a new unit replacing a non-operational unit will need to
be isolated and removed from energy savings and penalty consideration.
HVAC System Upgrade Lite
SCE recognizes the capital commitment for the customer to participate in CVC-HVAC. An
alternate method (HVAC System Upgrade Lite) can be provided, which isolates the scope of
work to duct design and renovation, and ongoing maintenance. The three-year maintenance
agreement continues to be a requirement to ensure persistence and collect data for validation
purposes, as described in the proposed CVC-HVAC strategy. This option is a critical middle
ground, provided to influence customers who previously replaced their HVAC units without
engaging in duct renovation or a maintenance agreement at the time.
A quality assurance process will be in place, to ensure end-use customers are not able to
participate in both ER/CUS and CVC-HVAC. If units are replaced within five years, customers may
participate in HVAC HOPPs Lite, which only considers duct renovation and a maintenance
agreement.
6.2.1
Additional Eligibility Requirement #1 (HVAC System Upgrade Lite)
If the customer or contractor wants to progress through HVAC System Upgrade Lite, they must
provide evidence of to- or above- code unit replacement within five years prior to the project
application submission to opt out of the required unit replacement for program participation.
Eligible incentives will be based on the overall system EER improvement.
Note #1: At the customer or contractor’s own discretion, a unit that is less than five years old
may still be replaced, and is eligible for program participation and incentives.
Note #2: Program Eligibility Requirement #2 still applies.
Claimed/Reported Energy Savings
Energy savings will be predicted ex ante, by leveraging existing work paper performance metrics
for unit replacement and duct renovation. Actual savings will be reported during ex post. After
one year of operation, NMEC analysis will be performed using the normalization methods
described in Attachment B to true-up actual energy savings at the meter.
20
6.3.1 Quality Assurance and Quality Control (QA/QC)
An appropriate minimum inspection rate will be required by the program, to verify system EER
in parallel with the maintenance vendor at the 12-month maintenance visit. If a case arises in
which the system EER does not materialize energy savings, the contractor will be given 30
calendar days to perform system remediation activities, and a second verification visit will occur.
If data does not support the materialization of energy savings, incentive installments will be
clawed back from the contractor.
6.3.2 Continuous Intervention, Equipment Testing, and Up-Sales
The CVC-HVAC program’s core innovation is structured around the continuous testing and
optimization, to maximize cooling delivery while minimizing energy consumption. Energy savings
will be a function of the overall system performance, as opposed to any one activity that may
have individually contributed to a portion of the total energy savings. This approach is a balance
between maximizing equipment efficiency and minimizing energy losses from the source to end
use. Furthermore, quarterly program intervention through the three-year maintenance cycle will
address ongoing system efficiency and any potential failing components, thereby ensuring
continual system optimization with minimal degradation. This comprehensive approach will help
to firm up continued energy savings.
6.3.3 Gas Savings Claims
The key benefit of the CVC-HVAC program is the impact to the Database for Energy Efficiency
Resources (DEER) peak demand, since the system is able to maximize the cooling delivered to
the space when electric load is at its greatest. Conversely, gas consumption is not expected to be
a dominant factor in influencing a customer’s decision to proceed with a project. Therefore, to
minimize potential free ridership claims, the utility has decided not to claim, or correspondingly
track, changes to gas consumption at the outset of this program.
SCE does not expect to collaborate with the gas companies on a project-by-project basis for
potential gas savings claims. When a representative sample size is obtained, data can be made
available to the gas companies for future assessment of incenting and claiming gas savings.
6.3.4 Program List of Measures
The Commercial HVAC HOPPs proposal objective is to promote long-term energy benefits
through a comprehensive systems solution. The energy efficiency measures are identified at a
high level, and may not be limited to activities explicitly stated in Table 1. The program is
designed to capture stranded energy savings achieved at a system level from non-prescribed
measures that currently cannot be captured in a work paper approach to savings claims. It aims
21
to credit both conventional and non-conventional system optimization “site specific” activities
that result in demand savings. At this time, however, SCE believes these represent the best
measure mix with the highest potential for persistent electrical energy savings.
Table 9 - Eligible Program Activity
End Use
Measure
Intervention Strategy
Savings Source
HVAC
Air-Cooled Packaged and Split
Systems < 5.4 Tons of Cooling
Capacity
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Air-Cooled Packaged and Split
Systems >= 5.4 Tons of Cooling
Capacity
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Water- or Evaporative-Cooled
Systems
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Variable Refrigerant (VRF/VRV)
Equipment
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
HVAC Early Retirement
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Evaporative Pre-Cooling for Aircooled Condensers
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Duct Renovation
Test In- Test Out
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Contractor Add VFD incentive
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Contractor Add DCV incentive
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
HVAC
Contractor Add VFD and DCV
incentive
Pre measurement (post-install) incentive and
Post-measurement incentive
Normalized metered based & on-site sub
metering equipment isolation
Projects are only eligible for incentive if equipment is installed, permitted (as needed), and
meets minimum California Title 24 Standards and Regulations. The contractor shall comply with
any and all required License or Code requirements, and obtain all necessary or advisable
permits.
Incentive Design
The CVC-HVAC program’s incentive design involves leveraging pre- and post- installation
performance measurement activity within the existing duct renovation deemed program. There
is a strong correlation between field-measured data and ex-post data. This characteristic enables
a percentage of the incentive to be paid shortly upon post-installation, which is critical for
compatibility with the cash flow and budget restraints that commercial building owners and
contractors encounter.
22
The field performance measurement will inform a model to be used to quantify payable
incentives to the customer upon installation completion. This same field performance data will
inform two out of three incentive installments for which a contractor is eligible. The third, final
incentive installment paid to the contractor is informed by comparison of the field performance
model to an NMEC model SCE would use for its savings claims. Incentive payments to the
customer and contractor are envisioned as follows.
The sum of the customer and contractor incentive must not exceed 100% of the gross project
costs. Any incentive that exceeds the gross project cost will be deducted from the contractor
incentive, until a net-zero project is achieved.
All costs associated with the project are reportable. Items on invoices should be itemized, to
clearly identify the equipment and supplemental materials needed to complete the scope of
work. Labor will clearly show the total labor cost and (as applicable) the labor buy down from
the customer incentive deferment.
6.4.1 Customer Incentive Structure
The customer incentive is paid 100% upon complete project installation. HVAC system
performance is usually not as heavily based on the customer’s activity as it is on the contractor’s
ability to perform optimization and routine diagnostics on the system. Therefore, any
subsequent incentive after the customer has already executed a contract is not expected to
impact the customer’s behavior. Incentive payment to the customer has been decoupled with
the NMEC. Contractors are responsible for the energy savings realization energy savings at the
meter, and are provided the necessary tools to execute via the ASHRAE Standard 180
Maintenance Agreement, which includes customer and contractor goal setting during the
maintenance term.
•
Paid in Full after Installation Completion = $75/ton
At the customer’s discretion, they may sign the eligible incentive to the contractor as a materials
and labor buy down. Incentives that are deferred to the contractor will be included in Contractor
Installment #2. Upon work completion, the invoice issued to the customer MUST depict the
material or labor buy down as a result of customer incentive deferment.
23
6.4.2 Contractor Incentive Structure
The contractor will receive 20% of a total incentive upon complete installation of the new unit8,
full system delivery diagnostics and duct renovation, and a fully-executed, three-year ASHRAE
Standard 180 maintenance agreement. The remaining 80% installment will be paid after delivery
of field performance measurements and survey results, and the incentive will be based on
normalized performance data. However, if energy savings does not materialize after one year of
normalized data analysis, or if the contractor fails to execute the three-year maintenance
agreement, the incentive payment paid to the date of determination will be clawed back.
•
•
Installment #1 = 20% forecasted energy savings
Installment #2 = (NMEC savings * $/kWh) – Installment #1
To further motivate the industry to target units with a high potential for energy savings, two
incentive levels are offered, with a premium available for greater magnitudes of performance
upgrade. There are two key elements that are addressed in this strategy:
1. Stratified incentive levels promote deeper energy savings for HVAC systems in greatest need
of overall system renovation. Units that can simply be replaced with minimal impact of overall
system performance do not yield a large incentive to the contactor.
2. Contractor incentives will ultimately be paid based on NMEC, which will be determined by the
internal utility or contracted program administrator based on a fixed NMEC methodology
(Attachment B). Therefore, over-estimation of energy savings which result in overpayment (or
gaming) on the first or second installments of a contractor incentive will be subject to clawback, if energy savings do not appear at the meter. This structure incents contractor
responsibility to perform quality installations, follow through with performance diagnostics
and optimization, and engage end-users on best practices.
CVC-HVAC incentive rates ($/kWh) offered to the contractor are subject to change, but are
envisioned to be greater than incentive rates available through prevailing non-residential HVAC
programs. This incentive value also offsets upfront cash flow risk that would otherwise present a
barrier to program participation.
8
All CVC-HVAC contractor incentive applications will be screened internally for minimum eligibility requirements
and for quality assurance purposes to prevent simultaneous enrollment in overlapping non-residential HVAC
programs.
24
Table 10 - Program Incentive Design
EER < 5
Test-Out
System
Efficiency
Test-In System Efficiency
5 ≤ EER < 6
6 ≤ EER < 7
7 ≤ EER < 8
Standard
Standard
Standard
8 ≤ EER < 9
Premium
Standard
Standard
EER > 9
Premium
Premium
Premium
Projected Incentive Levels9
 Standard Incentive = $0.80/kWh
 Premium Incentive = $1.00/kWh
Figure 8 - Program Incentive Timeline
Milestone #1
Milestone #2
Installment #3
Milestone: Equipment Installed
Milestone: Contractor Field Data
Submitted
Milestone: NMEC
Time: 0-3 months
Time: 13-18 months
Time: 12-15 months
Contractor Installment #1: 20%
Contractor Installment #2: 80%
Customer Payment: 100%
Customer Payment: 0%
Program Savings Potential and Program Objectives
Table 11 - Program Forecasted Energy Savings
2017
2018
9
2019
2020
2021
Program incentive design is subject to change based on guidance and comments from key Utility personnel and
Commission Staff guidance.
25
Forecasted Energy (kWh)
Forecasted Demand (kW)
171,000
427,000
1,223,000
1,280,000
1,280,000
100
300
800
900
900
Table 12 - Program Forecasted Budget10
2017
2018
2019
2020
2021
Administrative and
Implementation Costs
Marketing Costs
Incentive Costs
$85,000
$116,000
$245,000
$256,000
$256,000
$9,000
$46,896
$13,000
$298,683
$37,000
$737,068
$39,000
$1,494,036
$39,000
$1,616,813
Saving Measurements
and Verification
Total Program Budget
$277,000
$28,000
$70,000
$77,000
$77,000
$417,896
$455,683
$1,089,068
$1,866,036
$1,988,813
The program budgets are provided as best estimates for a new conceptual program launch.
Contingencies have been built in, to mitigate program budget overruns.
Administrative and Implementation Costs: $0.20/forecasted kWh with $50,000 for Year
1 development, and $30,000 for program design adjustments and concept finalization
after first-year launch analysis. The implementer costs include, but may not be limited to,
general program administration, project tracking, coordination with participating
contractors, and quality control procedures. These costs do not include Workforce
Education and Training (WE&T). An additional $266,000 funding source is needed to
continue a Duct Renovation Study that will inform the savings forecast.
Marketing Cost: $0.03/forecasted kWh.
Incentive Costs: See Section 6.4.2.
Savings Measurements and Verification Costs: 4% incremental program cost.
Based on budget figures and energy savings projections over five years, the program Total
Resource Cost (TRC) is 1.3311. After one year of program implementation, the utility will re-
10
Program budgets are best estimates for a new conceptual program launch. Contingencies have been built in to
mitigate program budget overruns.
11
E3 Calculator: SCE 2013 v1c6.xls. An assumption was made that 75% of tonnage will be Standard incentives and
25% of tonnage will be Premium incentives.
26
evaluate the program design to adjust requirements and budgets according to program success
and challenges encountered.
Enhanced Measure Effective Useful Life (EUL)
SCE’s savings forecast is tied to key performance measures informing EUL values described
below. By executing an ASHRAE Standard 180 maintenance agreement, contractor intervention
will continuously optimize and maintain the system for the first three years of installation,
thereby ensuring maximum performance with minimal degradation. Routine intervention is also
expected to identify and resolve equipment issues that may cause system malfunction or losses
due to equipment wear and tear. Typical measure EUL will commence after three years of
program intervention.
Because the ASHRAE Standard 180 maintenance agreement will provide continuous program
intervention to preserve the original system design performance, SCE believes the EUL of
standard measures will commence after three years. This already is a conservative design based
on the assumption that the customer will not renew the maintenance agreement after the first
three-year term. Thus, for this program, SCE proposes that the EUL of the equipment
replacement is still 15 years in addition to the first three years of program intervention.
Similarly, the EUL of the duct renovation is five years in addition to the first three years of
program intervention. Figure 9 illustrates “out-year” energy savings claims.
27
Figure 9 - Program Effective Useful Life
Energy Savings Claim Out-Years
100%
Unit
Replacement
To-code
90%
80%
70%
60%
50%
Unit
Replacement
Above-code
and Duct
Renovation
40%
30%
20%
10%
0%
1
2
3
3-Year
Maintenance Term
4
5
6
7
8
9
10
11
12
Typical Measure
EUL Start
28
13
14
15
16
17
18
Attachment B
Measurement and Verification (M&V) Plan
Savings Calculation General Method
The selected savings calculation method for this project is based on the International
Performance Measurement and Verification Protocol (IPMVP) Option C – Whole-Building Meter
Measurement. The ultimate goals of the program are to know if combining these technologies
saves energy while maintaining cooling capacity, and to measure the reduction in overall
building energy consumption as compared to the baseline. Therefore, building meter data for
one year prior to HVAC upgrades will be compared to building meter data one year after
upgrades. Additionally, differences in energy consumption due to weather (for example, outside
air temperature, dew point, and relative humidity) will be analyzed, and savings will be
normalized as needed.
Data Collection Strategy
Baseline & Post-Installation Data Collection
The baseline data collection period will be defined as at minimum one year prior to the start of
new HVAC equipment installation for each individual customer. Building meter data for
individual customers will be accessed retroactively via SCE. The reporting period will be, at
minimum, one year after all new HVAC equipment has been installed and is running on a
customer site. Due to changes in temperature from year to year, weather data will also be
collected from a weather station local to the customer site, and energy consumption data will be
normalized to give accurate comparative results.
SCE will systematically gather and use this collected data to establish post energy and baseline
usages with and without HVAC retrofits, respectively.
Un-normalized data will also be reported for archiving and re-evaluation as needed. The raw
data will be provided as supplemental information to each project submission, with no changes
to exported format or values.
Independent Variables
•
•
•
Outside Air Temperature [°F] (available from weather station(s) nearby)
Outside Air Dew Point [°F] (available from weather station(s) nearby)
Outside Air Relative Humidity [%] (available from weather station(s) nearby)
29
Static Factors
Static factors are independent variables which do not change, and hence do not impact energy
savings. These factors assume a constant or predictable behavior using DEER models and
building profiles. However, the reported energy savings will be based on metered data after one
year of collection.
Load
Aside from demand changes due to weather, power consumption from the HVAC system during
the baseline year is approximately equivalent to the reporting period year.
Operating Conditions
The program assumes the building operating schedule will remain constant from Year 1 to Year
2, and that the products sold do not have any effect on energy usage.
Building Envelope
It is assumed that the building envelope will not change from year one to year two. The building
envelope has an effect on energy usage, but it will not affect energy savings.
Equipment operating practices include:
• Equipment Schedules: Will ask customer what time the units turn ON and OFF.
• Cooling Set point: Will ask customer for cooling set points.
• Heating Set point: Will ask customer for heating set points.
Reporting Period
The reporting will be for at least one year starting after the completion of the HVAC retrofit.
Field Data Collection
This section provides the methodology and theory to evaluate energy savings using field data
collection. Note that Whole-Building meter consumption data will be used as the primary source
for energy savings claims, while reporting and field data collection serves as a secondary point of
verification.
Figure 10 is a basic overview of a standard HVAC system and the four components that are
evaluated during field testing.
30
Figure 10 - Field Data Collection Overview
System performance can be broken down into the separate components shown in Figure 10, or
evaluated at the system level. Field measurements capture each of these components.
Therefore, the “Equipment Delivered Capacity” is actually the “Field Measured Equipment
Delivered Capacity,” and the “System Delivered Capacity” is actually the “Field Measured System
Delivered Capacity.” The Field Energy Efficiency Ratio (Field EER) is taken as the most
representative method to forecast one year of energy savings.
Figure 11 - Relationship of RTU Energy Intensity to EER
EER vs. kW/ton
5
kW/ton
4
3
2
1
0
0
5
10
EER
31
15
20
Figure 12 - EER Point Descriptions
Point
Description
EER
Reading
1
EER
Reading
2
EER
Reading
3
System delivered capacity.
• No RTU replacement.
• No duct renovation.
System delivered capacity immediately after duct renovation.
• RTU replacement complete (unless HVAC System Upgrade Lite)
• Duct renovation complete.
System performance and delivered capacity efficiency three months after applying duct
renovation. Contractor visit is sponsored by maintenance agreement. Testing and
customer engagement will be required at each visit to ensure energy savings persistence
and to capture site variability.
System performance and delivered capacity efficiency 6 months after applying duct
renovation. Contractor visit is sponsored by maintenance agreement. Testing and
customer engagement will be required at each visit to ensure energy savings persistence
and to capture site variability.
System performance and delivered capacity efficiency 9 months after applying duct
renovation. Contractor visit is sponsored by maintenance agreement. Testing and
customer engagement will be required at each visit to ensure energy savings persistence
and to capture site variability.
System performance and delivered capacity efficiency 12 months after applying duct
renovation. Contractor visit is sponsored by maintenance agreement. Testing and
customer engagement will be required at each visit to ensure energy savings persistence
and to capture site variability.
EER
Reading
4
EER
Reading
5
EER
Reading
6
Basic System Parameters and Calculations
When evaluating the system performance, air delivery losses are subtracted from the measured
equipment performance:
= To evaluate the performance of the whole system, we compare the system delivered capacity to
the rated capacity of the equipment. This performance metric is referred to as the “System
Percent Delivered Capacity:”
=
System Percent Delivered Capacity can also be calculated as a product of the Equipment Percent
Delivered Capacity and the Air Delivery Efficiency:
=
Where,
32
∗
=
=
Capacity Measurements
Capacity cannot be measured directly, and therefore is calculated at different parts of the
system from changes in enthalpy and airflow, where airflow is measured in cubic feet per
minute (CFM).
Equipment capacity refers to the net capacity removed by the unit, in terms of thermal loads.
The basic formula for equipment capacity is:
= 4.5 ∗
∗
4.5 is a conversion factor to go from enthalpy and airflow to capacity, for units operating at sea
level. This conversion factor, the measurement techniques, and the measurement locations
change based on site specifics such as altitude as well as equipment configurations.
System delivered capacity is calculated in a similar manner to equipment delivered capacity, but
also takes into account duct air leakage (supply and return) and temperature losses to provide a
measure of an entire system’s useful cooling capacity. Expressed another way, system delivered
capacity represents the net total of the heat removed directly from the occupied zone and
includes interactions of the equipment, the outdoor air, the duct work, and the occupied space.
Wattage Measurements
In addition to capacity measurements, wattage measurements are also needed to calculate the
Field EER of a system. All wattage readings are calculated using the formula below:
=
∗
∗
∗√
The estimated power factor for all systems is currently 0.85. The program is looking into
measuring power factor, or measuring wattage directly in the future, but until this is
implemented, an assumption must be made. An estimate of 0.85 will be used until more
research can be done to determine a more accurate power factor. Power readings for threephase units must also be multiplied by the square root of three, to show the power being used
by all three phases (the square root of one is equal to one, and therefore single-phase units are
not affected by this calculation).
33
Measurement Conditions
Achieving reliable measurements for performance calculations requires that the unit is able to
run fully loaded and at relatively steady state. This typically requires that the unit be able to run
for at least five minutes prior to measurements being made, and that the outside air
temperature is at least 65°F. This outside air temperature is the commonly-accepted minimum
temperature required for verifying HVAC performance. One example of the 65°F minimum may
be found in the Title 24 2013 reference appendices, listing requirements for refrigerant charge
verification.
Data Requirements
Program participation requires technicians to measure all data needed to calculate equipmentand system-delivered capacity. Approximately 200 data points are collected on each system.
These data points include:
•
•
•
•
•
•
•
•
Customer contact information, job location, building data, and system identification.
Contractor and technician information.
Test conditions, time of day, elevation, and ambient temperatures.
Equipment nameplate, capacity and horsepower, manufacturer, and equipment type.
A full static pressure profile of the system.
A full airflow profile of the system, including economizer minimum and maximum outside air
settings, operating duct leakage, fan, and grille and register airflows.
A full wet-bulb and dry-bulb temperature profile of the system, including calculated entering
coil temperatures.
Fan speed and other measurements necessary to assess the system and identify defects.
Power measurements will support the calculation of a field-measured Energy Efficiency Ratio
(Field EER). Job documents and job data are used to determine what repairs are made on a
number of improved systems. This data is recorded and submitted after one year of postinstallation data collection at the Whole-Building Meter.
•
•
Test-In: Indicates the measured return airflow is low, and static pressure entering the
equipment indicates the resistance to airflow is high.
Test-Out: The return duct airflow has increased, and the return duct static pressure has
decreased.
The prescribed solution from the Test-In measurements is to either reduce blockages in the
duct, or increase the size of the return air duct. Further layers of verification are possible by
checking a change in the fan’s total external static pressure, an increase in filter pressure drop,
34
and a usual slight increase in the system supply air volume. These values help to determine the
measures that can be implemented.
Energy Efficiency Calculations Background
The Energy Efficiency Ratio (EER) is the common industry measurement to express the efficiency
of unitary HVAC equipment. The EER is the net cooling capacity, over total power usage.
Therefore, EER has the units Btuh/Watts:
= Where,
=
The prescribed energy savings per EER improvement will be forecasted using existing program
work papers and trued-up with NMEC.
Calculations, Regression Model, and Normalization
A random coefficients regression model (RCM) using 12 months of energy use data, and
corresponding ambient dry-bulb temperature (T) and other independent variable data will be
developed to estimate gross savings for each customer using their meter data. A detailed
description of this model is provided below.
The goal of the Energy Savings Calculations is to calculate normalized energy savings cfor each
site between the reporting year with the retrofitted HVAC system installed, and the baseline
year with heritage HVAC equipment.
Modeling Approach and Specification
The modeling approach described throughout this section has been excerpted and modified
from the AMI Billing Regression Study: Final Report, prepared by Evergreen Economics (2016)12
to incorporate other explanatory variables, such as changes in square footage, occupancy, field
measurement data, etc. It is important to note that the model presented here is one possible
model specification.
12
Available at http://www.calmac.org/publications/AMI_Report_Volume_1_FINAL.pdf
35
First Stage: Binning Process
In the first stage of our modeling approach, we use a fixed-effects regression model
to estimate daily baseload electricity use for each service account, controlling for
outside air temperature and other explanatory variables. The fixed-effects model
specification is as follows:
DailykWhi,t = αi + β1 (CDDi,t) + β2 (HDDi,t) + ∑ β k X i + εi,t
where :
DailykWhi,t = Daily kWh consumption for customer i on day t.
CDDi,t = Cooling degree days (CDD) for customer i on day t.
HDDi,t = Heating degree days (HDD) for customer i on day t.
αi = Customer specific constant (i.e., baseload normalized consumption)
Xi = Other explanatory variables (changes in square footage, field data, etc.)
β1, β2, βk = Coefficients estimated in the regression model
εi,t = Random error assumed normally distributed
A characteristic of fixed-effects models is the estimation of a specific constant, or intercepts
parameter, αi, for every customer site. This constant varies by customer site and accounts for
time-invariant effects on consumption. In the model specification above, the constant can be
interpreted as site-specific baseload consumption after controlling for variation in outside air
temperature (CDD and HDD, using a base temperature of 65 degrees Fahrenheit) and other
explanatory variables. Using statistical analysis, we estimate this constant, and obtain an
estimate of baseload energy use for each customer site.
Next, on a daily basis, we will characterize what each site experiences in terms of the weather
and day type. To create weather groups, we will compute the Cooling Degree Hours (CDH) for
each hourly observation using a base temperature of 65 degrees Fahrenheit, and then take the
average of these hourly values to create a single Cooling Degree Day (CDD) value for each site on
each day (in other words, each “site day”) in the study period. Next, we round the CDD up to the
next integer and assign it to a CDD group. For example, an hour with an outdoor temperature of
66.2°F would have a CDH of 1.2 (66.2°F – 65.0°F = 1.2). If the average of all 24 CDH is 1.4, it will
be rounded up to two, and will be assigned to CDD Group 2. For annual models, we will repeat
this process to assign days to Heating Degree Day (HDD) groups, again using a base temperature
36
of 65 degrees Fahrenheit. Categorizing days using outdoor temperatures in this manner
explicitly incorporates temperature into our modeling approach. To reflect possible differences
in energy usage between weekends and weekdays, we will also bin site days based on the day
type. Weekends will be assigned to day type Group 1, and weekdays will be assigned to day type
Group 0.
Lastly, site-day bins per site and per type of day are created. These bins will describe the sitedays in our sample, based on each site (baseline normalized energy usage), weather group (CDD
and/or HDD), and day type group (weekday versus weekend).
This binning process has the following benefits:
• Each bin will have only one site on one type of day. This means that variation in CDD is
controlled for in the bins, so it does not need to be included as a variable in the model
specification. The same is true for all other binning factors, like HDD, day type, and each
site’s baseline energy usage.
• We will be modeling site-days so we are able to exclude individual days with missing
observations from the data. For example, we can remove specific days with less than a
complete 24 hours of hourly data (such as removing days from a site’s data because they
have 22 hourly observations) rather than limiting the analysis to sites with flawless data
throughout the study period.
• When binning annual observations by site, weather, and day type, only one model is
required. The output is generated at the site level, so the model allows creation of load
shapes and savings estimates for each specific site (such as a specific combination of site,
weather, and day type), group, or at the program-level (for example, incorporating all
bins), without the need to run additional models.
• Participant sites with no post-period observations are still useful when constructing
models of the pre-period, because they are simply a series of site-days.
37
Second Stage: Random Coefficients Model
For the next stage, we randomly select 70 percent of site-days to be used in an RCM to develop
predicted hourly load shapes for each site-day bin using pre-period data. The remaining 30
percent of site-days (sites with similar energy usage patterns as the main site of interest) will be
set aside as a holdout sample to test the performance of the predicted load shape for each site.
In this way, we will ensure that the predictive power of the model is tested against data that was
not used to develop the model.
Finally, we will compute the hourly kWh value for each site in each site-day bin selected for
modeling. We specify a random coefficients model, because this approach will allow us to
simultaneously model the daily load shape (for example, hourly kWh usage) of each site-day bin
while accounting for covariance with other site-day bin load shapes. Unlike a typical fixed effects
regression, which produces a single set of coefficients and site-specific constants, the random
coefficients model produces a vector of regression coefficients for each site-day bin. Our final
random coefficients model specification will be as follows:
5
kW_Hri,t =
β
5
j,i (ChangeH i,t )
j=1
+
β
k,i (ChangeH i,t
* H i,t ) + εi
k=1
where :
kW_Hri,t = Energy consumption for site i during hour t.
ChangeHi,t = An array of dummy variables (0,1) representing hourly changepoints, taking a value
of 1 if an hourly observation falls between two changepoints. In our final model,
we use the changepoints 5am, 8am, 3pm, 6pm, 8pm, and midnight.
ChangeHi,t * Hi,t = An array of variables that interact the dummy changepoint variables with the hour
of the day.
β j,i , β k,i = Coefficients estimated in the model for site i.
εi = Random error, assumed normally distributed.
Using the above model specification, we generate coefficients for:
• The pre-period for the 70 percent modeling sample - we use these coefficients to test the
model’s ability to predict pre-period consumption in the 30 percent hold-out sample.
• The pre-period using 100 percent of service accounts - once we are satisfied with the
model predictions compared to the holdout sample, the full sample is then used to
estimate the model to take full advantage of all available data. We use these coefficients
38
to develop predicted post-period consumption in the absence of the energy efficiency
program intervention.
These coefficients are used to test the model and develop savings estimations as explained in
the following section.
Third Stage: Savings Estimation
The first step in our savings estimation approach is to test the predictive ability of our model.
We compare the holdout sample-predicted, pre-period hourly kWh values, developed using the
coefficients from the 70 percent modeling sample, with the actual pre-period hourly kWh values
of our holdout sample. If our model is performing well, the predicted pre-period hourly kWh and
actual pre-period hourly kWh should be similar, with any difference representing the error that
exists in our modeling approach. We create an hourly adjustment factor from this comparison to
account for any error, which we use later in the process to improve our modeling predictions.
We then subject the post-period data to the same binning process as we did to the pre-period
data (in the first stage). Each individual site remains in the same weather-normalized usage
group that they were assigned to in the pre-period, which helps isolate the effect of the program
intervention occurring in the post-period by holding the expected general usage constant
throughout the analysis period. Next, each day is assigned to a weather group (by CDD and/or
HDD) and day type group (for example, weekdays versus weekends).
After assigning each site-day in the post period to a site-day bin, in an identical fashion to the
pre-period data, we import the predicted hourly pre-period kWh values for each site-day bin in
the random coefficients regression model. We then multiply each prediction by our adjustment
factor, to correct for any error we found in our modeling approach (from the holdout sample).
This process gives us a predicted estimate of each site’s consumption during each hour of the
post-period if they had not participated in the program.
We compare the predicted post-installation hourly kWh values (based on the pre-period
consumption model and post-period weather data) with the actual post-period hourly kWh
values for each site. This is essentially comparing predicted site consumption, had the program
participation not occurred, to actual post-period consumption on days with the same weather
conditions and day types. When actual post-period consumption falls below the predicted
hourly kWh, this indicates energy savings during that hour attributable to the program. In
essence, the estimated site-level savings is the difference between the predicted post-period
hourly kWh and the actual post-period hourly kWh adjusted for any error found in the first step
of the savings estimation.
39
Threshold for Expected Savings
As described in the Savings Estimation section above, the threshold for savings depends upon
multiple factors: the amount of anticipated savings expected from the project, the accuracy of
the baseline and post-installation models used to calculate savings, the number of monitoring
points in the baseline and reporting periods, and the confidence level at which savings
uncertainty is reported. These factors combine to provide an estimate of the savings uncertainty
for each project. Discernable savings requires that the maximum allowable savings uncertainty
be 50% of the reported savings; however, this level of uncertainty is certainly too high for
stakeholders. The lower the uncertainty the better. With this proposed gross savings approach,
we plan to establish acceptable levels of uncertainty at the project level, as well as for the
population of program participants.
This methodology will enable the evaluation of typical rules of thumb that are used to establish
a threshold for savings, such as requiring a minimum of 10 to 15% savings on annual energy use
when using Option C methods with monthly data.
Baseline Adjustments
Baseline adjustments are categorized as routine and non-routine. Routine adjustments to energy
use are due to regular and expected changes in influential parameters. In many buildings, these
parameters include ambient weather conditions, production rate, and operating schedule. Data
for these parameters is collected and used to establish regression-based energy models that
describe how baseline or reporting period energy use is adjusted so that savings may be
calculated for a common set of conditions.
Non-Routine Adjustments
When unexpected or one-time changes occur during the reporting period, non-routine
adjustments to the energy savings must be made. Unexpected changes include static factors
which are not usually expected to change. For example:
• Changes to building size.
• Additions of heating and cooling loads in the building.
• Addition of load, such as computers or data processing equipment.
The baseline conditions of these static factors need to be fully documented during the baseline
period, and continually monitored for change throughout the reporting period, so that changes
can be identified and proper non-routine adjustments made. Condition tracking will be
performed by the building owner, a program M&V contractor, or the program’s implementer as
a part of the project. Engineering calculations will be used to quantify the energy impact from
such changes using retrofit isolation techniques, and used to adjust the meter-based energy
savings. To the degree possible, energy impacts from non-routine events will be calculated
based on actual measurements.
40
Net-to-Gross Adjustment for Net Energy Savings
The above energy savings calculation and methodology will derive the HOPP’s gross energy
savings. The proposed M&V protocol will go one step further to collect NTG data using a
generally-accepted NTG survey instrument at the end of project installation.13 The benefit of
this approach is timely free-ridership data collection before either memory or personnel
changes. This survey instrument will be designed to look at the degree of free ridership for each
project. The project will adopt generally-applied survey design and methodology based on CPUC
EM&V protocols. SCE understands that the CPUC is planning to conduct additional independent
impact evaluations to verify the reported gross and net energy savings.
Quasi-Experimental Design for Estimating Savings
Evaluability of the proposal will be enhanced with a quasi-experimental design using a matched
control group on energy consumption and general participant characteristics. In other words,
energy consumption from the treatment group (program participants) will be compared with
energy consumption of non-participants (comparison group) who were eligible to participate in
the program and show similarities with participants such as business type, square footage,
energy use, etc. Savings results from this design will be used to compare and verify results from
the RCM introduced earlier. In addition, energy use of participants in SCE’s QM and QI programs
will be used to inform the HOPPs proposal as a minimum threshold for savings, where
applicable.
13
The survey instrument will be developed following the framework in the California Public Utilities Commission
Energy Division’s “Methodological Framework for Using the Self-Report Approach to Estimating Net-to-Gross Ratios
for Nonresidential Customers,” prepared by the non-residential working group, dated Oct. 16, 2012.
41
Attachment C
REVIEW SHEET FOR 2016 HOPPs PROPOSALS
Compliance
Area
PA Proposal Requirements
SCE Comments
Principles of
HOPPs
1.
Proposal will increase energy
efficiency in existing buildings.
Described in Attachment A, Section 2
2.
Proposal references studies, pilots,
EM&V etc. that support the idea that
this project/program is a high
opportunity.
Proposal demonstrates how the
program/project will focus on
activities that are newly permissible
under CPUC code 381.2 (b), by
3a) Program/project will reach
stranded potential by utilizing the
new approaches to value and
measure savings.
Described in Attachment A, Section 4
3.
General
Program
Description
1.
Described in Attachment A, Section 2
3b) Focus on interventions that
PAs could not previously do.
Described in Attachment A, Section 2
3c) If proposal is a modification to
an existing program, then
proposal should clearly identify
the differences with the existing
program and benefits of the
proposal consistent with the
HOPPs principals.
Described in Attachment A, Section 2
Description of the intervention
strategy employed, with reference to
the type of known existing business
model being employed (e.g. Standard
Performance Contracting, ESCO
models, retro-commissioning,
experimental design, financing).
42
Described in Attachment A, Section 6
Compliance
Area
PA Proposal Requirements
SCE Comments
2.
Provides specifics on the terms of the
program structure.
Described in Attachment A, Section 6
3.
Explains how the project/proposal
addresses past challenges that have
arisen with the business model being
employed.
Described in Attachment A, Section 6
Measure
Treatment
1.
Measures and end uses that will be
addressed- describe what type of
intervention activities will be applied
to what measures. If implementers
propose to use deemed savings
values, then the DEER value applicable
to the site’s existing condition baseline
treatment must be identified (or an
alternative work paper offered per
CalTF vetting process).
Described in Attachment A, Section 6
Savings
Calculation
Methods
1.
For normalized metered energy
consumption, detailed description of
the savings calculation methods and
provide access to models used for
addressing normalized, metered and
energy consumption, detailed in
Attachment A.
For deemed savings projects that are
providing incentive payments based
on ex ante values, standard custom
project savings calculation methods
apply.
Described in Attachment A, Section 10,
and Attachment B
Basis and rationale for payment
structure--Explain the payment
structure, including the basis for
setting the upfront payment (if any)
and how the structure mitigates the
risk that potential upfront payments
do not overrun the value of the
realized savings.
Described in Attachment A, Section 6.4
2.
Incentive
Design
&
Customer
Incentives
1.
43
Described in Attachment A, Section 10,
and Attachment B
Compliance
Area
PA Proposal Requirements
SCE Comments
Attachment A
2.
Described in Attachment A, Section 6
Normalized
Metered
Energy
Consumption
Measure costs and capital burden;
identify the estimated capital costs,
the sources of capital funding the
project, and what portions of costs are
to be borne by ratepayer and by
implementer.
3. Partial or incremental payments with
true up over time; describe the terms
and schedule of the incentive
payments.
4. Strategy for tracking persistence;
describe the long term tracking and
reporting strategy for sustained
savings with ongoing feedback.
1. Programs and projects must
document the method for
normalization and list
a) the variables included in the
normalization process and
1b) Documentation of specific program
actions that are intended to drive savings.
2.
3.
4.
5.
6.
Models, methods, and tools must use
recognized engineering, economic or
statistical approaches to
normalization.
Models, methods and tools must be
transparent, reviewable and
replicable by peer reviewers.
In addition to normalized savings as
defined here, programs and projects
shall also report absolute changes in
consumption expressed with a
common denominator.
Models must include pre and postintervention data streams. Minimum
1 year post data for retrofits, and
minimum 3 years for Behavior Retrofit
or Operations.
Models, methods, tools must be
transparent, reviewable and
repeatable.
44
Described in Attachment A, Section 6.4
Described in Attachment A, Section 6.4
Described in Section 3, General
Description, and Attachment B
Described in Section 3, General
Description and Attachment B
Described in Attachment B
Described in Attachment A
Described in Attachment B
Described in Attachment B
Described in Attachment A
Compliance
Area
PA Proposal Requirements
SCE Comments
7.
Described in Attachment B
8.
9.
Type of
Program or
Project
1.
2.
Threshold for
Expected
Savings
1.
2.
Baseline
Adjustments
1.
2.
3.
Application to
Behavioral,
Operational,
Retro-
1.
Meter does not necessarily equal
Whole Building, so proposals must
make clear the link between meter
and building.
Proposals for programs or projects
must document the market barriers
they are designed to address and the
interventions planned to achieve
reductions in energy consumption.
If proposal deviates from Attachment
A, PA must provide clear rationale.
Description of the nature of the
proposed program or project
intervention with respect to WholeBuilding or single measures.
Site level results will be discernable at
building level for verification
purposes.
Description of the expected saving
from the proposed program or project
intervention.
Literature or field performance data
demonstrating the expected impact
and expected certainty of estimates.
Documentation of the baseline
assumptions and strategy for
collecting necessary information
Description of how normalization
methods capture (or not) baseline
assumptions
Description of the methods that will
be used to adjust the baseline for
non-routine adjustments, when
applicable for the type of proposal.
Program/project proposals shall:
Include requirement that participant sign
up for a maintenance plan for at least
three years.
45
Described in Attachment A, Section 3
Described in Attachment A, Section 3
Described in Attachment B
Described in Attachment B
Described in Attachment B
Described in Attachment B
Described in Attachment B
Described in Attachment B
Described in Attachment B
Described in Attachment A, Section 4
Compliance
Area
PA Proposal Requirements
commissioning
(BROs)
2.
3.
4.
SCE Comments
Program/project proposal shall:
Include requirement that participants
commit to install a minimum set of
measures according to PA pre-defined
criteria.
PA is encouraged to include a training
component to program/project
offerings.
Performance post-intervention:
N/A
Described in Attachment A, Section 5
Described in Attachment A
4a) Must ensure persistence of
savings that ensures multiyear savings
for measures that are based in
changes in behavior or operational
practices.
Financing
1.
4b) During the claimable Effective
Useful Life (EUL) period of one year,
continuous feedback should be in
place.
Described in Attachment A, Section 7.1
4c) PAs shall consider incentive
structures that encourage long term
savings
Described in Attachment A, Section 6.4
4d) Incentives shall only be paid once
participant commits to a maintenance
plan for a minimum of three years
(evidence should be made available to
Commission staff upon request).
Described in Attachment A, Section 4
Description of any use of financing
programs or external financing to
support the program or proposed
project.
N/A
46
Appendices
47
Appendix A: SCE Response to Review Sheet for 2016 HOPPs Proposal – Advice Letter 3463-E
This Appendix is the list of process issues identified during stakeholder interviews. The Table also includes current process statuses
and recommended action to address the issues.
Commission Staff Comments
SCE Responses
Principles of HOPPs
See Section 4.1
The incentive structure with final performance-based payment is new
and would not be allowable before. The services provided and
measures are a combination of existing programs and measures.
Attachment A section 2 doesn’t describe the stranded potential this
program is designed to capture.
General Program Description
How does [this program/proposal] overcome the challenges
experienced with the existing CQM, Upstream Rebate, and duct
sealing programs.
See Section 4.2
Measure Treatment
Replace on burnout situations are not mentioned. The CPUC guidance
is that savings to code can be accepted, but the existing performance
of a unit that would be replaced with no ratepayer funding must be
accounted for. The projected savings and payment should adjust for
replace on burnout situations
Incentives are the major driver for large capital investment projects such as a sitewide RTU replacement project. Energy savings that can be attributed to these
projects are by-in-large below code. Lack of financial support for this range of
improvement may detriment a project.
The proposal mentions customer incentives are based on a
combination of instantaneous EER testing and existing workpaper
methods, but doesn’t describe how the test results are applied to the
workpaper methodology.
The customer incentive is fixed metric as a function of tonnage. See Section 6.4.1
There is no existing workpaper that supports these figures at this time. Estimates
are based on non-resource program results from the Commercial Quality
Renovation program. A field validation study through the Commercial Quality
Need an example with references to a specific workpaper ID.
48
Installation program is currently in progress. The results are expected to firm up
these estimates.
Describe how DEER models will be selected and matched to the
customer site, and how equipment operating practices data will be
incorporated into the models.
Modeling approach and specification is provided in Attachment B which also
details consideration of operating practices. See Sections 11 and 13.
Model specification and normalization are detailed in Attachment B. See Sections
11 and 13.1.
How will the DEER models be trued up to billing data? What
calibration statistics will be used? Since the customer site and DEER
prototypes have different sizes, how will the results be normalized to
the unit being replaced?
Savings Calculation Methods
a.
Give some more information on how the EER measurement is
trued up with the AMI meter data.
b.
Provide a reference to the workpaper that is referred to as
providing the correlation of measured performance change
and savings.
c.
NMEC uses the random coefficients model developed by
Evergreen Economics under contract to SCE. Will the model
be made available for review? What platform (eg. SAS, SPSS,
etc) is needed to run the model? Can the model be used to
estimate savings of an individual building?
a.
b.
c.
The EER measurement data is used to continuously evolve the parametric model.
Modeling will continue to be refined with additional data inputs which ultimately
provides a truly representative baseline and measure as a function of various site
specific permutations.
The duct renovation workpaper is currently in the field verification test phase.
There is no approved workpaper at this time.
The model will be made available for review. The potential platforms under
consideration at this time are LimDep and R as outlined by the Evergreen report.
The model can be used to estimated energy savings of an individual building.
Please refer to the revised model specification in Section 12 in Attachment B and
memo received from Evergreen Economics in Appendix C.
Incentive Design and Customer Incentives
a.
How were the incentives amounts determined?
b.
What is the payment structure for the consumption based
estimate?
c.
How will money be clawed back?
d.
How will site-specific saving be estimated when the model is
pooled across sites
a.
b.
c.
d.
49
Incentive estimates are currently ~50% greater than a standard program which is
the maximum allowable incentive to maintain a cost effective program using the
proposed design.
20% / 80%
Claw back methods are currently being explored by the utility
SCE’s understanding, based on conversations with Evergreen Economics, is that
the RCM model can be modified such that it is possible to estimate savings for
each site and that model granularity is controlled by using site characteristics and
narrow ranges of cooling/heating degree-days.
e.
The customer incentives based on field measured EER
differences applied to DEER models are not at risk. This
upfront payment may overrun the value of the realized
savings model be used to estimate savings of an individual
building?
e.
The customer incentive is equivalent to existing program offerings through the
pre-existing CQI program. After careful consideration, the program design opted
to keep this design feature because customer behavior is extremely binary.
Customers typically respond to up-front incentives/rebates. Any money paid in
out-years will likely have little to no influence on customer decisions to proceed
with a project.
What are the implementer costs? Provide more detail in the proposal
budget, including total project costs paid by owner, and incentives
paid to owner and contractor that are consistent with the overall
program budget and forecasted savings shown in Attachment A
section 7.
Proposal Attachment A Section 6 Table 10 mentions dividing
customers into high, medium and low potential level categories for
contractor incentive payments. How will the potential level categories
be determined?
See Section 7
Quarterly EER testing for 3 years at sites with maintenance
agreement. How is persistence tracked for sites without maintenance
agreement?
The goal of the EER testing is to continue collecting data to further inform on the RCM
model. It is expected that these parameters will be collected for the preliminary stages
and the frequency and level of detailed data will be further explored by SCE staff
based on the success or failure of submitted projects have warranted such change.
See Section 7
The incentive levels are determined by the different EER rating prior to a immediately
after system level renovation and implementation.
Normalized Metered Energy Consumption
a.
List all variables to be used in the normalization.
b.
The role of the field tested EER in the random coefficients
model needs to be explained.
a.
b.
50
SCE has modified the random coefficients model to allow for normalization over
identified major criteria such as building use type, building square footage, and
occupancy in addition to weather. More site specific criteria to be collected
during the customer survey will be defined in the field data collection
specification. Please see revised model specification Section 12 in Attachment B.
The EER measurement data is used to continuously evolve the parametric model.
Modeling will continue to be refined with additional data inputs which ultimately
provides a truly representative baseline and measure as a function of various site
specific permutations. (as found in Slide 4)
a.
Proposal focuses on duct system renovation with optional
unit replacement. List of eligible measures in Attachment A
Section 6 Table 9 includes DCV and VFDs. How will these
measures be included in the renovation package? What
other repair activities such as refrigerant charge correction,
economizer repairs or renovations, control upgrades or
adjustments, will be included?
b.
Table 9 measures includes water or evaporative cooled
systems and variable refrigerant equipment. Suggest limiting
the activity to air cooled constant refrigerant flow unitary or
split systems.
c.
See Section 6.3, Table 9
The measures that are explicitly stated in the program proposal have been taken
from the existing HVAC portfolio. However, while these measures are included as
a fundamental list, the overall strategy of the program is to capture nonconventional measures that result in energy savings in addition to the standard
remediation activities. Therefore, any and all activities that improve system
efficiency may be included and is expected to be accounted for as a permutation
to the RCM.
b.
These items are included in order to capture a comprehensive range of
technologies currently available in the market. While these measures may be
underutilized, the utility finds it most appropriate to continue to include these
measures to enable to a full range of equipment upgrades.
c.
The sizing calculation are expected to be performed for each job to ensure that
appropriate airflow is delivered to the conditioned space. Furthermore,
contractors will use the appropriate standard design practices to size equipment
for the specific site.
a.
SCE will aim to include a comparison group as explained in section 16 in
Attachment B to control for unseen changes that may occur .
b.
SCE has modified the random coefficients model such that the model is able to
provide savings threshold by site. Please refer to the model specification in
Section 12 in Attachment B.
c.
The EER measurement data is used to continuously evolve the parametric model.
Modeling will continue to be refined with additional data inputs which ultimately
provides a truly representative baseline and measure as a function of various site
specific permutations.
Attachment A Section 6 Figure 7 mentions “right sizing” as a
value added activity. Will sizing calculations be done on every
job? What techniques will be used to size the systems?
Comments on the AMI study led to some items being deferred to
Phase 2 of the study. It is unclear how those issues are addressed. A
few key items are:
a.
There is no attempt to use a comparison group, why not
since the model developed is a pooled regression
b.
It says non-routine adjustments will be made by site and
savings threshold by site, but how can this be achieved with a
pooled model?
c.
a.
How are engineering measurements used in the RCM model?
The methods used to develop the field EER are more fully described in
the documentation for the CQR pilot. Reference or include that
document as an appendix
See Appendix B: Estimated Uncertainty of Field Measured HVAC System Efficiency
51
Make clear that un-normalized data will also be reported. No
reference to changes expressed with a common denominator made in
Attachment B.
The text says up to one year and not at least one year, needs revision
See Section 9.1
Link between meter and building must be shown.
Units replaced may only affect a section of a large building which
would reduce relative savings at the meter into a tight range
Include a process to survey the meter numbers at each site and
identify which meters serve the renovated space.
See Section 6.2
See Section 9.1
Type of Program or Project
Types of equipment in the program. How gas will be treated. How
economizers will be treated via ACCA 180? How VFDs will be treated.
Gas is not included in the program design. Each aforementioned component
(economizers and VFDs) is expected to be a permutation that can be captured using
the RCM.
The key benefit of the CVC-HVAC program is the impact to the DEER peak demand
since the system is able to maximize the cooling delivered to the space when electric
load is at its greatest. Conversely, gas consumption is not expected to be a dominant
factor in influencing a customer’s decision to proceed with a project. Therefore, to
minimize any free ridership issues, the utility has decided not to claim, and
correspondingly track, changes to gas consumption.
Whole-Building analysis relies on detectable energy savings above the
noise in the metered data. What is the savings threshold for the RCM
applied to a single building? Describe how will the field measured EER
be projected into annual savings and compared to total building
consumption to forecast an adequate savings threshold.
Minimum thresholds for this program are defined as 5% annualized energy
consumption and 10% peak demand. Repercussions for failing to meet these standards
are still under discussion by the utility.
Threshold for Expected Savings
a.
Accuracy of EER test are?
b.
Multiple data sources (existing workpaper, CQI database) are
referenced as having been evaluated, but there is no ex post
evaluation of these yet. References should be cited
See Appendix B: Estimated Uncertainty of Field Measured HVAC System Efficiency
a.
52
The specific accuracy of field EER test results will be assessed in tandem with
the ongoing CQI Field Data Collection Test. This risk is mitigated by using the
performance based model that ultimately utilizes the revenue grade meters
to quantify energy savings.
c.
Results from CQR pilot should be cited to document the
expected change in field measured EER. Include an
uncertainty analysis projecting the measured EER uncertainty
from the measurement uncertainty. Deemed power factor is
not acceptable. True electric power measurements of unit
consumption must be made.
b.
Documentation from field test will be referenced and provided as information
from the parallel program path becomes available
c.
Results of the CQI field test are currently pending. These artifacts will be
included in the final launch proposal.
Baseline Adjustments
Who to ask to determine past year thermostat settings. Future
settings. How will the need for non-routine adjustments be
identified? Any process or production level information required in
addition to weather to normalize the billing data (could be important
in light manufacturing or restaurants)?
Details such as these are expected to be included in the on-site survey that the
contractor will be required to administer as a condition for program participation.
Thermostat settings and ad hoc system adjustments are admittedly difficult to
pinpoint. However, contractors are the main program beneficiaries and, being the
customer facing entity, are expected to provide an educational aspect to customers to
drive market transformation. The survey is to be administered at 3 periods to collect
site changes over time: (t=0 months), (t=6 months), and (t=12 months). After an
appropriate level of confidence has been achieved in the RCM model, survey
frequency may be revisited for program budget considerations.
RCM focuses on weather normalization. Describe how other
normalization variables will be included.
How will changes other than temperature be accounted for (now says
engineering calculations). See above, site specific adjustments for the
pooled model are unclear
See Section 12.1
See Section 12
Application to Behavioral, Operational, Retro-commissioning (BROs)
a.
The assumption about out year performance should be trued
up in the future with data. Will all participants be tagged for
future study?
b.
Also can replacement in the traditional program be
compared to those in this program that have to agree to
QM? A quasi-experimental design analysis would be feasible
and quite useful
a.
A short term plan will flag all submitted applications for future study. The sample
size in subsequent years are expected to be sized appropriately based on the
success or shortcomings of preliminary study group.
b.
See Section 16
SCE is willing to explore the option of comparing the results from this proposal to
that of participants in the QM and QI programs as such analysis is already
proposed under the Phase 2 of the AMI Billing Regression Study. Results from the
53
QM and QI program participants have the potential to inform the HOPPs proposal
as a minimum threshold for savings where applicable.
Additional Comments from Review Team
a.
Method for developing EUL not consistent with commission
policy.
b.
Describe how to avoid double counting of savings from
Upstream program with HVAC unit replacement is done.
c.
Renovation activities will likely affect gas consumption. Will
changes in gas consumption be tracked? Reportable by SCG?
d.
EER testing done on a quarterly basis. Will all measurements
(including supply/return register air flows) be repeated on a
quarterly basis?
e.
RCM should be compared to LBNL Time of Day and
Temperature model, which may be more applicable to single
building analysis.
f.
On filing page enter the utility type
g.
How does this overlap with the existing CQM program? (p.17)
h.
Need more information on the EER test – how performed,
who will do it, weather effects (p. 17)
i.
No documents cited for QI
j.
P.8 shows image of a Gas Pack, but this is an electric utility
(explain)
a.
b.
c.
d.
e.
f.
g.
h.
i.
j.
54
See Section 7.1
See Section 6.2
The key benefit of the CVC-HVAC program is the impact to the DEER peak demand
since the system is able to maximize the cooling delivered to the space when
electric load is at its greatest. Conversely, gas consumption is not expected to be a
dominant factor in influencing a customer’s decision to proceed with a project.
Therefore, to minimize any free ridership issues, the utility has decided not to
claim, and correspondingly track, changes to gas consumption.
See Response for “Type of Program or Project”
See Attachment B
Agree
CVC-HVAC HOPPs and CQM have overlap when considering the Maintenance
Agreement. However, where CQM considers the Maintenance Agreement the
primary driver of the program, CVC-HVAC HOPPs utilizes the Maintenance
Agreement as a non-incentivized program participation requirement that is
intended to promote persistence and continuous system improvement.
Furthermore, a potential program requirement element that may be incorporated
is the restriction of participation by customers who currently have a 3-year
agreement that was financially supported by the utility.
See Appendix B
See Response for “Threshold of Expected Savings”
Figure 3 of the proposal is provided as a close representation of the multiple
activities that may place in a standard maintenance agreement. To clarify, units
that are replaced or serviced can be electric or gas heating. However, the credit to
the contractor is specifically for electrical energy savings. Any gas consumption
byproduct will not be tracked or reported in this program since this parameter is
not expected to be a key decision point.
Appendix B: Estimated Uncertainty of Field-Measured HVAC System
Efficiency
BENJAMIN LIPSCOMB, P.E. – NATIONAL COMFORT INSTITUTE
Introduction
This document describes the approach and results taken by National Comfort Institute to
estimate the uncertainty of a Field-Measured and Calculated Energy Efficiency Ratio (Field EER)
metric developed by NCI. The metric is the primary performance indicator proposed for
Southern California Edison’s (SCE) Commercial Quality Installation (CQI) and Comprehensive
Value Chain HVAC (CVC-HVAC) programs. Standardizing the data requirements, test method,
and calculations to support the metric is also an objective of two parallel HVAC industry efforts:
1. Western HVAC Performance Alliance (WHPA) CQI Standardized Field Data Spec and
Performance Evaluation Method Working Group
2. ASHRAE SPC 221 Committee – A Test Method to Measure and Score the Operating
Performance of an Installed Constant Volume Unitary HVAC System
Background
Over the past 20 years, NCI has been developing approaches, training and certification to
enable technicians to measure the performance of HVAC systems in the field. The Field EER
metric is the most recent evolution of these approaches, and is intended to facilitate the
evaluation of a system’s in-situ efficiency by technicians who have been properly trained and
certified to do so. This metric may be used for a variety of applications, including energy
efficiency programs.
Recognizing that the Field EER metric is a key aspect of multiple utility programs and industry
standards under development, NCI perceived that the uncertainty of the metric would become
a topic of discussion and undertook an internal effort to estimate its accuracy in April of 2016.
In this paper we share the methodology and results of that effort.
Field EER Calculations
The precise methodology for calculating Field EER is one subject of the previously mentioned
industry standardization efforts. At a high level Field EER is understood as:
=
55
→
Where:
=
thefield-measuredandcalculatedcoolingcapacitydeliveredbythesystemtotheoccupiedzone
= thefield-measuredpowerusedbythesystem
=
referstoaprocessusedtoprojectperformancefromthemeasurementconditionstostandardratingconditions
Details of the supporting calculations have evolved slightly since the uncertainty analysis was
completed. Ultimately, the final calculation methodology defined by standardization efforts
may be able to reduce uncertainty relative to what we are reporting here by eliminating some
inputs and/or reducing some of the error associated with the calculations.
Methodology
A propagation of error analysis estimates the uncertainty of the result of a function that
includes two or more input parameters that are themselves uncertain. Classical analytical
methods for propagating error are the simplest form of this type of analysis, but the application
of these methods becomes cumbersome when the function is complex. Because the calculation
of Field EER involves several complex steps that cannot be represented as simple algebraic
expressions, we used a statistical approach called Monte Carlo simulation to estimate
uncertainty. The Monte Carlo simulation was conducted using Oracle’s Crystal Ball add-in for
Microsoft Excel.
The Monte Carlo simulation uses a model that calculates Field EER based on a variety of fieldmeasured and calculated inputs. The method allows one to specify the distribution of the
uncertainty for inputs to the model. The simulation then randomly varies each input within the
specified distribution for the desired number of simulation iterations. The output is a
distribution representing the uncertainty of the result. Figure 13 is a graphical representation of
the output of the Monte Carlo simulation. This output shows the possible distribution of the
true Field EER for a scenario in which the measured Field EER was approximately 6.9. At the
90% confidence level, the true Field EER is somewhere between 6.0 and 7.8.
56
Figure 13 – Graphical Example of the Monte Carlo Simulation Output
Assumptions
Our analysis assumes that the measurements are executed by a qualified technician who is
specifically trained and certified to test the performance of HVAC systems in the field. NCI
provides training and certification for both residential and commercial system performance,
and in our experience training and certification is of critical importance to achieving the level of
precision for each of the inputs considered by this analysis. If measurements are not executed
by an individual with NCI or equivalent training and certification, the results of this analysis do
not apply.
The analysis also assumes that a consistent measurement procedure is followed using
calibrated test instruments meeting a defined set of instrument specifications. The WHPA and
ASHRAE efforts, as well as program documentation to support the SCE programs, will all
provide clear, written procedures that will provide the basis for best field practices.
Estimation of Input Parameter Uncertainty
In estimating the uncertainty of input parameters, we recognize that there are three types of
error that can contribute to the overall uncertainty:
1. Instrumentation error.
2. Measurement technique error.
3. Calculation error.
57
We grouped the inputs to the model into categories of inputs that would be subject to
instrumentation error only, inputs subject to both instrumentation and measurement
technique error, and inputs subject to calculation error.
Inputs subject to instrumentation error only are inputs that are less sensitive to variability due
to measurement technique or field conditions. Some of the inputs in this category are still
subject to additional uncertainty if sensor placement or measurement technique is incorrect,
but training and certification can largely minimize the chances of this happening. The
uncertainty estimates in this category are generally derived from NCI instrumentation
requirements that are provided in our training. We are recommending similar requirements in
the ASHRAE, WHPA, and utility program development activities that we are involved in.
Inputs subject to both instrumentation and measurement technique error are inputs that
have uncertainties significantly higher than the instrumentation error alone. The additional
uncertainty for these inputs is a result of variability in technician technique and skill, even after
training, as well as the impact of field conditions on the accuracy of the measurement. For
example, duct and equipment configuration can increase the uncertainty for fan airflow
measurements. Wind can increase the uncertainty of an outside airflow measurement. The
uncertainty estimates in this category rely on data from third-party studies on measurement
uncertainty where available, or NCI’s own accuracy estimates based on our field experience
where no applicable studies were found.
Inputs subject to calculation error are the result of the process of normalizing the measured
Field EER to rating conditions. This process relies on a characterization of the system’s
performance as a function of operating conditions such as outside air temperatures and
evaporator entering air temperatures. For this analysis, we used performance maps of HVAC
systems developed by the DEER team to support their energy simulation efforts. The
uncertainty values we use are the approximate average uncertainty of these performance maps
across their applicable range. It should be noted, however, that uncertainty is reduced if
measured conditions are close to rating conditions. This detail is not reflected in this analysis.
Table 1 below summarizes the input variables and their associated uncertainty estimates, and
includes a note for each regarding the source, rationale, and/or other aspects to consider. Each
uncertainty estimate was assumed to represent the 95% confidence interval on a normal
distribution. 95% confidence is the level typically used to report instrumentation error.
Table 13 – Model Inputs and Uncertainty Estimates
Instrumentation Error Only
58
Input Data Field
Uncertainty Note
Estimate
Dry Bulb and Wet +/-2%
Bulb
Temperatures
NCI Instrumentation accuracy requirement, assumes no additional
measurement error. Requires proper sensor placement.
Supply and
Return Register
CFM
+/-5%
NCI Instrumentation accuracy requirement, assumes no additional
measurement error. Requires proper measurement technique,
adaptation to field conditions such as irregularly shaped registers and
grilles.
Unit and Fan
Power
+/-3%
NCI Instrumentation accuracy requirement, assumes no additional
measurement error.
Altitude
+/-10%
Conservative estimate of smart phone GPS accuracy, assumes no
additional measurement error.
Instrumentation and Measurement Technique Error
Input Data Field
Uncertainty Rationale
Estimate
Fan Airflow
+/-10%
NCI estimate of uncertainty for fan curve airflow plotting method or
direct measurement using a pitot or hot-wire anemometer traverse
under non-ideal field conditions. These are the two methods
recommended by NCI. In practice, the fan curve plotting method is
used most of the time. Calculation methodologies currently being
considered by standardization efforts would eliminate this input and
its associated uncertainty.
Outside Air CFM
+/-15%
Estimate based on CASE Study14 results for OA CFM using velocity
grid and hot-wire anemometer. These are the two methods
recommended by NCI.
Calculation Error
Input Data Field
Uncertainty Rationale
Estimate
Equipment
Capacity & EER
Normalization
+/-10%
NCI estimate of average error in DEER15 performance curves, error is
0 at design conditions, and is higher for lower or higher than design
conditions. Calculation methodologies currently being considered by
standardization efforts would eliminate the equipment capacity
normalization uncertainty.
14
California Utilities Statewide Codes and Standards Team, October 2011, Outside Air.
DEER2015 update documentation, 2015DEER-PackagedAndSplitDXUpdate-24Nov2014.xlsx. Retrieved from:
http://deeresources.com/files/DEER2015/download/2015DEER-PackagedAndSplitDXUpdate-24Nov2014.xlsx
15
59
System Capacity +/-15%
Normalization
Similar to above, but NCI estimates an additional 5% error because
we do not currently normalize duct thermal losses or gains to rating
conditions as.
Data and Simulations
NCI used a convenience sample of data that we had on hand for 46 tests conducted by HVAC
field technicians on real systems as the base data set for simulations. These data represented
tests conducted on 23 commercial HVAC systems before (Test-In) and after (Test-Out) various
system renovation activities were implemented. Table 14 summarizes the Test-In and Test-Out
Field EER, and the improvements achieved by the renovation activities.
Table 14 – Summary Statistics for Test Data
Test Data Summary Test In Field EER Test Out Field EER % Improvement
Mean
4.4
7.1
73%
Min
2.3
4.8
-9%
Max
7.3
9.4
247%
Standard Deviation
1.4
1.1
59%
While this is a convenience sample, we believe that the results shown are fairly representative
of typical results in California and nationwide. The uncertainty results are not necessarily
dependent on this sample being representative, but later we will compare the uncertainty to
the improvements to show that the estimated uncertainty is appropriate given the differences
between Test-In and Test-Out Field EER that we were trying to detect.
For each of the 46 tests, we conducted 1,000 iterations of the Monte Carlo simulation analysis,
for a total of 46,000 simulations. Each simulation used the measured inputs and results for the
initial values, and the simulation varied the inputs randomly about the initial value, and within
the uncertainty distributions previously discussed.
Results and Discussion
The average plus and minus percent uncertainty across all 46 tests are reported in Table 15
below. We chose to report the results at the 90% confidence level because this is the
60
confidence typically used for reporting on evaluation, measurement, and verification (EM&V) of
energy efficiency programs.
Table 15 - Field EER Uncertainty Estimate
Mean Uncertainty
90% Confidence
+
Field System EER
12.4%
-12.6%
Rounding the results up, we typically state that the estimated uncertainty of our test method is
+/-13% at the 90% confidence level. Since the plus and minus percent uncertainty are very
close, the results indicate that there is very little bias error inherent in the test method.
Applying this uncertainty to the mean Field EER results from our test data set of 23 units, it is
apparent that the uncertainty of the results are low enough to verify the significance of the
average improvement observed in the sample. This indicates that the test method is generally
adequate as a method to verify the improvements of an HVAC system renovation. Table 16
illustrates the low, average, and high improvement that we would expect on a system with
Test-In and Test-Out EER results equivalent to the averages of our test data.
Table 16 – Uncertainty Estimate Applied to Sample Average Test-In and Test-Out Field EER
Values
Low
Improvement
Average Result
High
Improvement
Test In EER
5.0
4.4
3.8
Test Out EER
6.2
7.1
8.0
24%
61%
110%
% Improvement
Conclusions and Recommendations
Our analysis indicates that uncertainty of the Field EER test method is approximately +/-13% at
the 90% confidence level when the method is executed by trained and certified HVAC
technicians in accordance with a standard protocol. We believe that this is a reasonable
estimate of the uncertainty based on our own field experience, and that this analysis represents
the best available data on the uncertainty of field-measured HVAC efficiency.
61
On the topic of using the Field EER metric to track performance for energy efficiency programs,
we have several recommendations that we believe will help to achieve a reasonable level of
accuracy consistent with this analysis. These recommendations are as follows:
•
•
•
•
•
•
Establish a minimum improvement level that will result in an acceptable level of risk of
detecting improvements when there are none. The level established should take the
uncertainty estimates presented into account.
Establish clear procedures for Field EER measurement.
Establish training and certification requirements that ensure technicians develop and
demonstrate the fundamental skills necessary to execute the procedure.
Utilize a software tool that allows technicians to record measurement data and
performs all calculations necessary to arrive at Field EER.
Put in place a quality control procedure for checking the reasonableness of multiple
inputs to spot potential gross measurement or data entry errors. Consider incorporating
some algorithms in the technician interface to alert them when an entry may be in
error.
Develop a linkage between QA/QC outcomes and training and certification metrics.
In closing, we would like to express our opinion that the uncertainty of this method can be
further reduced in the future through improvements in instrumentation, equipment
performance data availability, and the increasing prevalence of onboard monitoring systems on
HVAC equipment. That said: we believe that the current state of field-measured HVAC
efficiency represents the most accurate viable method to understand and quantify the
performance of existing HVAC systems and the improvements that may be realized by a skilled
workforce.
62
Appendix C: Random Coefficients Model for AB802
MEMORANDUM
Date: Nov 14, 2016
To: Emrah Ozkaya, SCE
From: Steven Grover
Re: Random Coefficients Model for AB 802
The purpose of this memorandum is to provide additional information about how the random
coefficients model would support an AB 802 type of project, where savings must be estimated
for individual sites/customers.
Commonly-used regression analysis methods in energy efficiency evaluation, such as fixedeffects regression analysis, fit a single line through a scatter of data, producing a single impact
estimate that is applied universally to all participants and days being analyzed. This approach
implicitly assumes that the value of each estimated coefficient is constant across the units of
observation in the analysis. While this assumption simplifies the analysis, it is often unrealistic to
assume the coefficients do not vary across customers, seasons, or hours of the day. The
random coefficients model differs from commonly used methods in that it fits a unique
regression line to each load shape while simultaneously accounting for correlations in energy
use across all load shapes. These disaggregated values provide a richer picture of energy
impacts by separating savings estimates based on customer type, day type, day temperature,
and season.
The random coefficients model process starts with customer segmentation. At the most basic
level the method groups customers by total annual energy consumption and average seasonal
load shapes. Depending on the information available, the groups can be refined using other
known characteristics such as building type, square footage, or HVAC type. Next we group
observed days by day type (weekend vs. weekday) and the actual weather conditions on that
day (heating degree-days and cooling degree-days). These groupings remove a substantial
amount of uncertainty from the model by reducing the variation in energy usage across
customers and days. Separate models are then estimated for each customer usage/daily
weather combination, producing separate predictions for each.
In a previous study, the random coefficients model was used to simultaneously generate load
shapes for over 1,000 different combinations of customer types and daily weather conditions.
When estimated load shapes were compared against a holdout sample of customers, the model
performed extremely well; hourly energy load estimates were within one percent of the actual
load for the holdout sample. After finalizing the model, we estimated savings for each individual
63
customer and day by comparing their predicted load shape (1 of 1,000 generated by the model)
to their actual energy usage on that day. The population-based savings estimates presented in
the report come from an aggregation of these customer-specific estimates.
In the context of an AB 802 type project, where savings need to be quantified for individual
sites/customers, the AMI data for the model would include the participant of interest and a large
pool of similar customers, selected for their energy usage characteristics. The random
coefficients model would generate a series of load shapes for the program participant, one for
each set of conditions that they experienced. We would test the effectiveness of the model on a
randomly selected holdout sample of similar customers and individual days from the participant
in the pre-period. Based on these comparisons, the customer-specific models and segmentation
criteria can be adjusted to improve their forecast accuracy for that individual. Once the model is
finalized, we will estimate savings by comparing the customer-specific model predictions for
each day in the post-period to what the participant actually used.
If there are any additional questions about this modeling approach, please contact myself or
Sarah Monohon at [email protected] or by phone at (971) 888-7478.
64