Chapter 2 - Policies for Sustainable Construction
Dividing indoor comfort limits by climate zones and describing it
as a curve for the benefit of passive and low tech architecture design.
Gustavo Linhares de Siqueira
Hafencity Universität, Resource Efficiency in Architecture and Planning, Hamburg, Germany
[email protected]
Udo Dietrich
Hafencity Universität, Resource Efficiency in Architecture and Planning, Hamburg, Germany
[email protected]
ABSTRACT: In the recent years great amount of research has been undertaken in the fields of
thermal comfort indoors. It is largely accepted that in natural ventilated buildings where occupants are allowed to directly influence their environment, the comfort expectations tend to follow outdoor conditions. The adaptive comfort model relies on these observations and generated
two major standards: EN 15251 focused on Europe and the ASHRAE 55-2004 worldwide applicable.
The first part of this paper introduces the ASHRAE 55-2004 and its background field research.
The second part discusses the vantages and disadvantages of dividing the available comfort limits by climate groups for the benefit of climate responsive design. The last part presents evidence for a more suitable comfort limits' description through a single-curve function instead of
the available braked-in-two-seasons linear model.
1 INTRODUCTION
Humphreys’ research in the 1970s (Humphreys 1978) was pioneering in the field of so called
Adaptive Comfort. Firstly he proved that comfort state is related with the experienced indoor
conditions. Secondly, since in naturally ventilated buildings the indoor conditions vary according to the outdoor climate, he found out that comfort sensation varies according to the outdoor
conditions as well. The formerly widespread belief that indoor comfort would be steady-state
and independent from outdoor conditions, following only physiological efforts to maintain
thermal balance, was objected by Humphrey’s work. Unlike the existing theory of thermal balance, which refers only to experiments in a climatic chamber, Humphrey’s research is based on
field surveys.
In the years that followed, many other field surveys have been undertaken in different climate
zones and building types, which supported the adaptive hypothesis.
2 RELATED WORKS
In 1997 the American Society of Heating, Refrigerating and Air-conditioning Engineers
(ASHRAE) commissioned a number of field surveys, which originated the RP-884 database (De
Dear et al 1998) and later led to the first standard in adaptive comfort: ASHRAE Standard 552004. This Database contains the results of worldwide surveys mainly performed in office
buildings.
Since 2007 the ASHRAE 55-2004 has a European counterpart: the EN 15251. Considering
the research design, the basic differences are the criteria how to classify buildings (type or operation modi) and the methods to calculate both the comfort temperature and the outdoor tempera-
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Fig. 1 – Comparison between two adaptive comfort limits in the major comfort standards (De Dear 2011)
ture. Both of the comfort models have been developed on the base of research in office buildings, however they are intended to be applied to all sedentary activities including dwelling.
Despite the different calculation methods both standards present a comfort zone divided into
two seasons: the naturally ventilated (or “free-running”) and the heated or cooled season. During the naturally ventilated season the comfort zone’s pattern is inclined whilst the heated or
cooled season presents a flat profile. Note in Figure 1 the similarity of the resulted formulae.
(For more detailed description see Humphreys et al 2010, Nicol & Humphreys 2010, and De
dear 2011).
3 HYPOTHESIS
This paper works on two hypotheses valid only for naturally ventilated buildings, in which indoor environment is influenced by outdoor climate:
1. Since seasonal changes are fluid and do not suffer abrupt changes it is probable that a
curve would be more adequate to represent the comfort function than the two lines
model of both existing adaptive comfort models.
2. As stated by the adaptive theory, people tend to adapt their comfort expectations according to given conditions. Therefore, it is sensible to believe that people in different
climate types present different comfort patterns.
4 METHODS
The proof of the first hypothesis begins with analysis of the RP-884 database.
This database is available online in a group of separated files classified in many categories
such as: location, building type, year and climate type. The given climate classification was
substituted by the more widespread Köppen-Geiger classification. Moreover only the major
climate groups were used and the cold climates were put together resulting in:
A- humid-hot
B- arid-hot
C- temperate
D- cold
The aim of this paper is to evaluate the comfort patterns of users who are used to adapt themselves dynamically to climate variations, hence only the data from naturally ventilated buildings
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were considered. This explains the absence of the D category in the experiment, as none of the
buildings in research projects located in the cold zone (D) were naturally ventilated.
Each file in the raw data represents a large amount of parameters, and the most important
were: user’s comfort vote (in a 7-point scale ranging from -3 “too cold” to +3 “too hot”), indoors temperature (real time), outdoor temperature (day mean) and air speed.
To find the comfort temperature only the votes between -1 and +1 were considered and
represented in combination with the indoor temperature in which they were applied. The
represented indoor temperature is an adjusted operative temperature, which is the given operative temperature, after the influence of air speed upon comfort sensation has been reduced. This
reduction is described in the following formula (EN 15251).
⎛
⎝
θin = θi −⎜ 7 −
⎞
⎟
4 + 10 * V ⎠
50
(1)
For V>0,5 m/s
V=Air speed, θin=adjusted indoor temperatur, θi=measured operative Temperatur (raw database)
The comfort temperature is commonly represented at the y-axis.
The x-axis is used to represent the outdoor temperature. In this case, it is based upon the
rounded mean outdoor temperature recorded on the raw file. The rounding helped to cluster the
comfort votes in 1K intervals and to form a grid. Thereby it is possible to define, for each outdoor temperature value, the mean comfort vote and the standard deviation.
Subsequently, as shown in Figure 2, the mean comfort curve is calculated as a polynomial regression upon the mean comfort votes. The mean standard deviation is used to define the comfort range, i. e. the comfort limits above and below the mean comfort curves.
An enhancement (Dietrich 2010) was the substitution of the polynomial functions by Arc Tan
functions. It came to be very useful since, unlike the polynomial functions, which started to
change the direction as the local maximum and local minimum are achieved, the Arc Tan curves
just change the direction twice.
Fig. 2 - The different patterns for the three main climate groups: A, B, C.
The resulted formulae for A:
θi = -0,0239θo3 + 1,8668θo2 - 47,529θo + 421,34
⎛
⎞
⎟⎟
⎝ π * arctan[0,37 * (θo − 25,7)] ⎠
θ = 26,5 + ⎜⎜ −
i
9
(2)
(3)
The resulted formulae for B:
θi= -0,0014θo3 + 0,0911θo2 - 1,2127θo + 23,316
(4)
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⎛
⎞
17,5
⎟⎟
⎝ π * arctan[0,15 * (θo − 21,7)] ⎠
θ = 25,25 + ⎜⎜ −
i
(5)
The resulted formulae for C:
θi = -0,0024 θo³ + 0,1393 θo² - 1,9593 θo + 27,392
⎛
⎞
14
⎟⎟
⎝ π * arctan{0,18 * [0,19 * (θo − 20,4 )]} ⎠
θi = 26 + ⎜⎜ −
(6)
(7)
Where: θi= comfort temperature, θo=mean outdoor temperature.
5 DISCUSSION
After the comfort limits for the three different climate groups have been defined as shown before, they can be used for an evaluation of the thermal performance during the design process.
This process is presented schematically in Figure 3 and will be described in the following.
The first step would be the climate classification itself into the climate groups A, B, C or D
(Fig. 3, #1). The second step is the linkage between climate and the basic design statements.
These design statements refer to the Mahoney tables (Königsberger et al 1971) and to Eproklid
(de Siqueira 2010). The direct linkage to the different climate zones has been described less explicitly than in the Mahoney tables, however it is adequate for its present purpose, being only a
quick start-up for further optimization. (Fig. 3, #2)
Subsequently the indicted design strategies are related to appropriate passive conditioning
strategies as shown by Givoni (1998) (Fig. 3, #3).
At this point follows the first evaluation due to a thermal simulation combined with the proposed comfort limits discussed in paragraph 4. (This simulation requires detailed definitions of
parameters such as air change ratio, internal gains, user’s profile etc., which could be object of
another research.) If the result of this evaluation turns out as “hot” or “cold”, either the available
design and the passive strategies can be optimized, or, if necessary, one of the low energy conditioning strategies introduced by Givoni (2011) can be applied (Fig. 3, #4).
If the next evaluation shows that the comfort state can be achieved by no means, an active
conditioning strategy has to be chosen (Fig. 3, #5).
In the following the process itself will be described for each of the three climate groups.
As the outdoor climate in the A-group (humid-hot) usually stays close to acceptable comfort
limits it is basically necessary to prevent extra thermal stresses. Therefore sun protection and
light materials are used to avoid thermal loads. The large opening ratio required allows permanent cross ventilation as a passive conditioning strategy. Those recommendations guarantee the
enhancement of the comfort. If necessary, an indirect evaporative system can be used as low
energy conditioning. Givoni and Gonzales showed positive results in an experiment in Maracaibo, Venezuela in these climatic conditions (Givoni 2011).
For the B-group (arid-hot), as both annual and daily swings are usually large, it is reasonable
to combine high thermal mass with good sun and wind regulation. In the cold season sun radiation is allowed to warm up indoors and daytime ventilation is preferred, whilst in the hot season
sun radiation should be completely avoided and nocturnal ventilation prevails. These passive
conditioning strategies could be complemented with direct or indirect evaporative cooling systems, if comfort state cannot be achieved in the hot season.
For the C-group usually similar strategies as for the B-Group can be applied except that more
thermal insulation and less thermal mass are needed mainly in the coldest zones of this climate
type.
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Fig. 3 - Schematic presentation of the design process using proposed comfort limits
6 CONCLUSIONS
The paper presents some evidence that support both hypotheses.
Firstly, since all the functions show high level of confidence (r²>0.9), it can be stated that a
curve is suitable to describe a comfort function.
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Secondly, as the pattern’s differences between the three curves are remarkable, the paper’s
second hypothesis is also verified. Note in Figure 4 that the A-function is very short and inclined whilst the B-function is both longer and flatter.
Moreover, it has been shown that by using the proposed climate comfort limits an effective
and practical design-tool can be created, which here has only been explained schematically. The
detailed description of this design-tool could be subject for a further research.
Two points are still unclear:
1. The adopted comfort ranges are compromising: 1.5K (A), 3K (B) and 2.7K (C). De Dear
(2011) shows how to quantify the enhancement of comfort limits due to elevation of air speed.
It is arguable though, if the comfort ranges are constant parallel to the mean comfort.
2. As the RP-884 is focused on surveys in office buildings, it is desirable to investigate if the
patterns originated in other functions e.g. dwellings would diverge.
Fig. 4 - The Arc tan curves overlay for comparison of those patterns
REFERENCES
de Dear, R.J. 2011. Recent Enhancements to the Adaptive Comfort Standard in ASHRAE 55 - 2010 .
45th Annual Conference of the Architectural Science Association, ANZAScA 2011. The University of
Sydney.
de Dear, R.J. Brager, G.S. & Cooper, D. 1998. Developing an adaptive model of thermal comfort and preference. ASHRAE RP-884 Final Report.
de Siqueira, G.L. 2010. Entwurfsprozess mit Klimadaten. Masterthesis. Hafencity Universität Hamburg.
Dietrich, U. 2010. Internal discussion.
Humphreys, M.A. 1978. Outdoor temperatures and comfort indoors. Building Research and Practice 6:
92-105.
Humphreys, M.A., Rijal, H.B. & Nicol, J.F. 2010. Examining and developing the adaptive relation between climate and thermal comfort indoors. Windsor, UK.
Nicol, J. F. & Humphreys, M.A. 2010. Derivation of the adaptive equations for thermal comfort in freerunning buildings in European standard EN15251. Building and Environment, 45.
Koenigsberger, O.H., Mahoney, C. & Evans, M. 1971. Climate and House Design. New York: United nations
Givoni, B. 1998. Climate Considerations in Building and Urban Design. New York: Van Nostrand Reinhold Co.
Givoni, B. 2011. Indoor temperature reduction by passive cooling systems. Solar Energy 85: 1692-1726.
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