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Proceedings of 8th
Windsor Conference: Counting the Cost of Comfort in a changing world
Cumberland Lodge, Windsor, UK, 10-13 April 2014. London: Network for Comfort and
Energy Use in Buildings, http://nceub.org.uk
Application of the adaptive model proposed by ASHRAE 55 in the
Brazilian climate context: raising some issues
Renata De Vecchi1*
, Márcio J. Sorgato1, Miguel Pacheco
1, Christhina Cândido
2 and
Roberto Lamberts1
1 Federal University of Santa Catarina, Department of Civil Engineering – Brazil;
2 The University of Sydney, Faculty of Architecture, Design and Planning – Australia;
* Corresponding email: renata.vecchi@labeee.ufsc.br
Abstract
This paper evaluates the adaptive method application proposed by the last version of ASHRAE 55 (2013)
standard in two different climates in Brazil. ASHRAE 55 (2013) currently allows for linear and exponential
methods to calculate the prevailing mean outdoor air temperature (Tpma(out)) and both are used to establish the
acceptability zones. For the exponential method, two different α were used (0.6 and 0.8). Moreover, the Tpma(out)
was calculated for two different time spans (7 and 30 days). Results indicate that choosing linear or exponential,
and even a monthly method, to calculate the Tpma(out) is indifferent when small amplitudes are concerned; but it
can lead to different limits of acceptability when significant day to day temperature variations are present.
However, the impact on the sum of discomfort hours is minimal. In addition, the results of this paper indicates
that it is possible to find significant percentages of thermal acceptability from previous field study below the
lower limit of acceptability proposed by the ASHRAE 55 (2013) adaptive model. Thus, a clo adjustment zone is
suggested.
Keywords: thermal comfort, ASHRAE 55 adaptive model, prevailing mean outdoor air temperature, clo
adjustment zone.
Introduction
In the last 3 decades, many questions regarding the applicability of Fanger's (1970) thermal
comfort model in buildings with reduced energy use through passive conditioning resulted in
the rise of a different model, extensively known as adaptive model (de Dear et al., 1997;
Brager and de Dear, 1998; Nicol et al., 2012). Since then, the adaptive concept has been
incorporated in the main thermal comfort standards, as ASHRAE 55 Standard (subsequently
the 2004 version), ISO 7730 (subsequently the 2005 version) and EN 15251 (subsequently
the 2007 version). Among the outlined standards, ISO 7730 remains with a superficial
version of the adaptive concept with no updates since then, while others adopted the model
based in extensive field results. The European standard EN 15251 (2007) is based on results
of the European Union project Smart Controls and Thermal Comfort (SCATs), which aimed
to reduce energy consumption in air conditioning systems by adjusting the building’s internal
temperature in line with outdoor temperatures through an adaptive algorithm (Nicol and
Humphreys, 2010). ASHRAE 55 (2013) is based on the results of ASHRAE RP-884, which
analysed a dataset with global and climatic variability, containing over twenty thousand
entries of the indoor microclimate and the occupant’s sensation, preference and acceptability
(de Dear et al., 1997). In Brazil, the thermal comfort standards are outdated, and no mention
about the adaptive model is found. However, Lamberts et al. (2013) published a research
report that present a Brazilian Standard proposal, written based on ASHRAE 55 (2010). At
moment, NBR 16401 (ABNT 2008) that deals with HVAC design is under revisions, and the
proposed standard was sent to the committee.
ASHRAE 55 is a widely adopted standard to assess thermal comfort in buildings, and this
fact occurs mainly due to the constant updates and revisions of the document, reflecting the
newest results from field experiments in thermal comfort area. In its current version,
ASHRAE 55 (2013) presents two graphical methods: one for thermal comfort evaluation in
all spaces where average air speed do not exceed 0.2 m/s (Figure 1); and another one is
specifically for naturally ventilated environments (Figure 2). The main modification from
last published version related to naturally ventilated environments is the adoption of a
prevailing mean outdoor air temperature (Tpma(out)), calculated by an average of the immediate
previous days (starting with yesterday, the day before yesterday and continuing backwards
until the last day of the chosen time span). To establish a comfort zone of a specific day
considering 80% of acceptability, 3.5ºC are added and subtracted to the prevailing mean
outdoor air temperature.
Figure 1. Graphic comfort zone method for
determining acceptable thermal conditions in spaces
with low air speed. Source: ASHRAE 55 (2013).
Figure 2. Acceptable operative temperature ranges
for naturally conditioned spaces. Source:
ASHRAE 55 (2013)
The prevailing mean outdoor air temperature is an essential parameter to assess the thermal
comfort condition based in the adaptive model; in the last revision, ASHRAE 55 (2013)
presented a method that let the users freely select the best calculation form, which differ in
calculation process of the prevailing mean temperature and in the time span chosen. It can be
determined by the use of a simple arithmetic average (which can be obtained by a range of
averages considering at minimum 7 and maximum 30 days prior to the day in question) and,
as an exception, it is also possible to use an exponential weighted average. When the weather
data to calculate the prevailing mean temperature are not available, the standard permit the
use of published meteorological monthly mean temperature. The difference between the two
equations proposed involves the weighting of immediate previous days: the simple arithmetic
average weights all days equal, while the exponential average weight the days closer to the
reference day, according with the α value set (which can vary from 0.6 to 0.9).
Since the prevailing mean outdoor air temperature was incorporated in adaptive model of
ASHRAE 55 (since addenda C from ASHRAE 55/2010), few studies evaluated the
applicability of this method in the Brazilian context. Lamberts et al. (2013) analysed a series
of thermal acceptability votes from Brazilian field experiments in different climates, noticing
some discrepancies related to the model. According with the authors, the adaptive
opportunities played a major role in these thermal environments, particularly by clothing
adjustments (blue dots) and the air speed value (orange dots). The new version of ASHRAE
55 (2013) incorporated an increase in the upper acceptability limit related to air speed, but the
lower limit remains as an issue not discussed yet. In this context, this paper focuses
specifically on ASHRAE 55 (2013) adaptive model application in Brazilian climatic
conditions
Figure 3. Thermal acceptability for naturally ventilated buildings with Brazilian field data.
Source: Lamberts et al., 2013.
Method
This study is mainly based in the application of ASHRAE 55 (2013) adaptive model in two
Brazilian cities with different climatic conditions. The results were used to compare all the
prevailing mean outdoor air temperature calculation methods, considering the number of
hours in which environmental conditions are outside the comfort zone (cold and hot
discomfort). Furthermore, data from field experiments carried out in one of the analysed
cities was used to verify the applicability of the adaptive graph from ASHRAE 55 (2013).
Climates description
According with the Köppen-Geiger (Peel et al., 2007) climatic classification, the Brazilian
territory has eight climates variations with different features. In this paper, a city from the
North and a city from the South were chosen (Figure 4).
Florianópolis is an island located on the south of Brazil (latitude 27°40'S; altitude 7m),
classified as a humid subtropical climate. The city presents humid and rainy summers due to
unstable air masses, and mild winters. Relative humidity is high throughout the year (about
82%) and there is no dry season. The highest rainfall occurs from October to March and the
lowest from July to August (mean annual precipitation is 1521mm). Belém, located in North
of Brazil, has a tropical rainforest climate (latitude -1°40’S and altitude 16m), and is a city
that suffers a direct influence of the Amazon rain forest. For this reason, the rainfalls are
constants (mean annual precipitation is 2889mm) and the humid seasons noticeably, spanning
from December through May. The others months present a small reduction in precipitation
volume. Average temperatures have no significant variations throughout the year, presenting
an annual mean temperature around 26.5°C.
Figure 4. Brazilian’s climatic classification according with Peel et al. (2007) and location of selected cities.
Data evaluation and the thermal acceptability limits of each climate were set by weather files
in TRY format (Test Reference Year - TRY). The files were obtained from a climatic
database of a typical year, available for download at the Laboratory of Energy Efficiency in
Buildings (LabEEE)1 electronic address.
1 http://www.labeee.ufsc.br/downloads/arquivos-climaticos/formato-try-swera-csv-bin
Prevailing mean outdoor air temperature
The prevailing mean outdoor air temperature (Tpma(out)) was determined according with the
three ASHRAE 55 (2013) methods:
Method 1: A simple arithmetic mean of all mean daily outdoor air temperatures
( , calculated with 7 and 30 sequential days prior to the day in question.
Method 2: The weighting method in which the exponential value ( ) were set in 0.6
and 0.8. In this method, twenty sequential days before the day in question were used
to avoid residual losses.
The exponentially equation used from ASHRAE 55 (2013) is the following:
[ ] (1)
Note: In the “ ” part of ASHRAE’s 55 equation, there is an instead
the , which the authors considered as a typing error and replaced according.
Method 3: The published meteorological monthly means for each calendar month.
Adaptive model acceptability limits
The acceptability limits were determined according with the equations from ASHRAE 55
(2013) considering the 80% acceptability limits (Equations 2 and 3). To determine the 90%
limits, 1°C were subtracted from each limit
Upper 80% acceptability limit (°C): (2)
Lower 80% acceptability limit (°C): (3)
Field experiments
The thermal comfort votes used to verify the applicability of adaptive graph from ASHRAE
55 (2013) were carried out by De Vecchi 2011 and results from thermal history and cooling
preference in HVAC were published by De Vecchi et al. (2012). The field experiment was
conducted in Florianópolis in 2010 collecting a total of 2.292 votes. The data were separated
in two groups: data with air-conditioning use (n = 1.200) and data without air-conditioning
use (n = 1092). For the purpose of this paper, only the acceptability votes from the group
where no air-conditioning was used were selected (n = 920, 84% of 1.092 votes). During the
experiment, the air speed average was approximately 0.6 m/s, and this value was considered
to adjust the acceptability upper limit related to the air speed value, as ASHRAE 55 (2013)
determined.
Hours of comfort quantification
The comparison was performed by quantifying the discomfort hours considering the
acceptability limits delineated by ASHRAE 55 (2013). Thus, the authors assumed a
hypothetical situation where the building indoor operative temperature is exactly the same of
outdoor air temperature, ignoring the internal loads and assuming good natural ventilation
throughout the year. In Belém's quantification - where the annual higher temperatures are
favourable to natural ventilation – all the months were considered. In Florianópolis, only the
summer months were considered (from December 21st to March 20
st), focusing in the periods
in which the buildings can be operated predominantly with natural ventilation.
ASHRAE 55 (2013) allows the quantification of the number of hours in which environmental
conditions are outside the comfort zone requirements for adaptive model during the occupied
hours in the time period of interest (Exceedance Hours – EH method). To this end, the
following calculation from ASHRAE 55 (2013) was used:
(4)
Where: = 1 if Top > tupper and 0 otherwise, and
= 1 if Top < tlower and 0 otherwise;
and are the discomfort hours outside the zone boundaries and the units are
in hours. Although, the method proposed on the standard only quantify the total of discomfort
hours, which were separated by cold discomfort hours (the values) and hot
discomfort hours (the values) by the authors. The occupation period considered
in this paper in the daytime is from 7am to 7pm (commercial buildings) and in the night-time
from 7pm to 7am (residential buildings).
Results and Discussion
Initially, the prevailing mean outdoor air temperature was calculated from all methods
described and for each of the analysed climates. Figure 5 and Figure 6 show the differences
between the prevailing mean outdoor air temperatures calculated by the arithmetic average
considering the last 7 and 30 days prior to the day in question, the exponentially weighted
running mean temperature with α set to 0.6 and 0.8 and the monthly means for each calendar
month. Analysing Figure 5, it is observed that the exponentially weighted and arithmetic
(considering 7 days) methods showed similar results, reproducing the outdoor air temperature
behaviour. Among then, the line with α set at 0.6 is the one that is closest to the outdoor air
temperature, providing a prevailing mean temperature with a faster response for the last
recent days. In Belém, where no significant variation in external temperature are observed
throughout the year, all the methods results in similar values of prevailing mean temperature
(Figure 6).
Figure 5. Prevailing mean outdoor air temperature from Florianópolis’s climate.
Figure 6. Prevailing mean outdoor air temperature from Belém’s climate.
The prevailing mean outdoor air temperature impacts directly in the lower and upper limit of
acceptability zone, and such results can be observed in Figure 7. The range represented by the
darker color characterizes 90% acceptability and the light color range 80% acceptability.
When the acceptability zone is defined from exponential method (α 0.6) it is possible to
observe a greater oscillation in the upper and lower zone limits, which followed the external
temperature fluctuations (Figure 7a). When α was set to 0.8, these oscillation are smaller, but
still visible. A similar small oscillation was observed in Figure 7c, when the zone boundaries
were calculated from arithmetic average considering the previous 7 days. The arithmetic
average which considered the last 30 day showed a smooth variation throughout the year
(Figure 7d), and the monthly means showed an acceptability zone with monthly steps (Figure
7e).
a) Florianópolis, α 0.6 b) Florianópolis, α 0.8
c) Florianópolis, 7 days average d) Florianópolis, 30 days average
e) Florianópolis, monthly means
Figure 7. Adaptive comfort zone defined as a function of the prevailing mean outdoor air temperature.
The results found in Florianópolis and Belém were plotted in the adaptive comfort graph
assuming that the indoor operative temperature is exactly the same of external air temperature.
The prevailing mean outdoor air temperature was calculated with the exponential weighted
method and α set to 0.6. Figure 8 shows the results considering only the commercial hours in
both cities for the warms months (between December 21st and March 20
st) in Florianópolis,
and all the year in Belém. From Figure 8b, it is possible to observe points that characterize a
cold discomfort situation when the prevailing mean temperature is superior to 25°C and the
indoor operative temperature superior to 22°C. Florianópolis presents a similar situation, but
with a lower number of points in this zone.
a) Florianópolis b) Belém
Figure 8. Cities climate data plotted in the adaptive graph from ASHRAE 55 (2013).
The thermal comfort votes used in this study to verify the applicability of adaptive graph is
shown on Figure 9. The acceptability votes represent 84% from 1092 analysed data; when
plotted in Figure 9 graph, there is a small number of votes that exceed the upper limit (3% of
the votes considering the same prevailing mean temperature conditions), and a significant
number that exceed the lower limit (24% of the votes considering the same prevailing mean
temperature conditions). However, when air speed is significant the Standard proposed an
adjustment to the upper limit fixed in 1.2°C when the average air speed is 0.6 m/s, while the
votes below the lower limit are characterized as cold discomfort. In situations like these one,
these votes can be explained by the garment adjustment (clo), and such results have also been
observed in Lamberts et al. (2013). Thus, Figure 9 also proposes an adjustment to these votes
based on the clo adaptation (orange zone), and based in the lower limit of the 1.0 clo zone
proposed by the graph method for environments with low air velocity, set in 19.5°C for 80%
of R.H (seen Figure 1).
Figure 9. Thermal acceptability votes from the experiment of De Vecchi (2011) plotted on the acceptable
operative temperature zone for naturally conditioned spaces and the proposed zone for the clo adjustment.
The discomfort hours was quantified by the number of exceedance hours in which
environmental conditions are outside the comfort zone during the occupation time (daytime
and night-time). These analyses were made to all the calculations forms of the prevailing
mean temperature, and the hours were separated by cold discomfort, and hot discomfort
(Tables 1 and 2) Table 1 presents the results of Florianópolis considering the hours of the
warm months. Table 2 presents the results of Belém city considering all the hours from a full
year.
Table 1. Data quantification to Florianópolis city (daytime and night-time).
Daytime Period (7am - 7pm) α 0.6 α 0.8 7 days 30 days Monthly
(Cold discomfort/hours) 114 116 131 104 104
(Hot discomfort/hours) 214 195 205 234 211
% of hours outside the acceptability zone 28.0% 26.6% 28.7% 28.9% 27.0%
% of hours inside the acceptability zone 72.0% 73.4% 71.3% 71.1% 73.0%
Total hours analysed 1170 1170 1170 1170 1170
Night-time Period (7pm – 7am) α 0.6 α 0.8 7 days 30 days Monthly
(Cold discomfort/hours) 253 256 265 233 233
(Hot discomfort/hours) 0 0 0 0 0
% of hours outside the acceptability zone 25.6% 25.9% 26.8% 23.5% 23.5%
% of hours inside the acceptability zone 74.4% 74.1% 73.2% 76.5% 76.5%
Total (hours) 990 990 990 990 990
In Florianópolis, the difference between the exceedance hours resulted in values with no
significant differences in both periods analysed (daytime and night-time). In night-time
quantification, the arithmetic 30 days mean presented the larger number of discomfort hours,
value 3.3% superior of the lowest one in the 30 days average and monthly means. Conversely
in Belém, the arithmetic average considering the 30 days and the monthly mean were the
ones that resulted in the smaller number of cold discomfort when compared to Florianópolis.
The difference between values from these two methods in the cold discomfort hours was
about 7% during night-time.
Table 2. Data quantification to Belém city (daytime and night-time).
Daytime Period (7am - 7pm) α 0.6 α 0.8 7 days 30 days Monthly
(Cold discomfort - hours) 286 315 317 118 118
(Hot discomfort - hours) 1487 1408 1404 1861 1795
% of hours outside the acceptability zone 37.4% 36.3% 36.3% 41.7% 40.3%
% of hours inside the acceptability zone 62.6% 63.7% 63.7% 58.3% 59.7%
Total (hours) 4745 4745 4745 4745 4745
Night-time Period (7pm – 7am) α 0.6 α 0.8 7 days 30 days Monthly
(Cold discomfort - hours) 47 101 114 157 157
(Hot discomfort - hours) 301 322 328 5 9
% of hours outside the acceptability zone 8.7% 10.6% 11.0% 4.0% 4.1%
% of hours inside the acceptability zone 91.3% 89.4% 89.0% 96.0% 95.9%
Total (hours) 4015 4015 4015 4015 4015
If used to the exceedance hours calculations, the proposed clo’s adjustment zone from Figure
9 eliminate all the cold discomfort throughout the year in Belém (considering all the 24 hours
of occupation); and, in Florianópolis, the cold discomfort hours drops 72% (244 hours),
considering data from 30 day column and 24 hours of occupation.
Conclusions
This paper presented the application of adaptive method from ASHRAE 55 (2013) in the
Brazilian climate context. Some previous results raised a series of issues, which led to a
deeper analysis in two different climates: an island located in the South of Brazil
(Florianópolis) classified as a humid subtropical climate and a tropical rainforest climate
located in North (Belém), with an annual mean temperature around 26.5°C. Thus, the
following topics were discussed: 1) the different methods to calculate the prevailing mean
outdoor air temperature, their results and implications on ASHRAE 55 (2013) acceptability
zone; 2) the lower limit from ASHRAE’s adaptive graph and the adjustment suggested based
on the clo adaptation.
As a general comment, the exponentially weighted equation with α set to 0.6 is the method
with a faster response to the recent days; however, when the sums of hours quantification is
compared considering the monthly means method, no significant differences were found
(2.1% of difference in discomfort hours between the two methods in Florianópolis). Choosing
a linear or exponential method to calculate the prevailing mean outdoor air temperature is
indifferent when small amplitudes are concerned; but it can lead to different limits of
acceptability when significant day to day temperature variations are present. However, even
with these zones displacements, the impact on the sum of discomfort hours is minimal. The
exponential method, regardless of the strong relation with the external air temperature, is
more laborious to be applied by the users of the Standard.
Also, the results indicated that it is possible to find significant percentages of thermal
acceptability data below the lower limit of the zone proposed by the ASHRAE 55 (2013)
adaptive model. Thus, an adjustment in the lower limit of 80% acceptability was proposed
considering that before the occupant use any other form of adaptation, the first thing they will
adapt is the garment. This adaptation occurs instinctively, explaining the votes of
acceptability outside the lower limit. Based on the results of this paper, the proposed clo’s
adjustment zone can eliminate more than 70% of discomfort data in warms months in
Florianópolis, and 100% of discomfort data from Belém throughout the year. The lower limit
of the acceptability zone from adaptive model represents a contradiction if compared to the
lower limit stipulated by the graphic comfort zone to spaces with low air speed. In this
graphic, the indoor operative range occur between 19.5°C and 26.5°C to 1.0 clo and between
25°C and 28°C to 0.5 clo.
References
ABNT, 2008. Instalações de ar-condicionado - Sistemas centrais e unitários - Parte 2:
Parâmetros de conforto térmico, NBR 16401-2, Rio de Janeiro: Associação Brasileira de
Normas Técnicas.
ASHRAE Standard 55, 2010. Thermal Environmental Conditions for Human Occupancy,
Atlanta, Georgia: American Society of Heating, Refrigerating and Air-Conditioning
Engineers.
ASHRAE Standard 55, 2013. Thermal environmental conditions for human occupancy,
Atlanta, Georgia: American Society of Heating Refrigerating and Air-Conditioning
Engineers.
Brager, G. & de Dear, R., 1998. Thermal adaptation in the built environment: a literature
review. Energy and Buildings, 27(1), pp.83–96.
De Dear, R., Brager, G. & Cooper, D., 1997. Developing an adaptive model of thermal
comfort and preference: Final Report on ASHRAE RP - 884, Sydney: MRL.
De Vecchi, R., 2011. Condições de conforto térmico e aceitabilidade da velocidade do ar em
salas de aula com ventiladores de teto para o clima de Florianópolis/SC, Dissertação de
Mestrado. Departamento de Engenharia Civil, Universidade Federal de Santa Catarina.
Florianópolis, 2011.
De Vecchi, R., Cândido, C. & Lamberts, R., 2012. Thermal history and its influence on
occupants’ thermal acceptability and cooling preferences in warm-humid climates: a
new desire for comfort? In Proceedings of the 7th Windsor Conference: The changing
context of comfort in an unpredictable world. Cumberland Lodge, London UK.
EN 15251, 2007. Indoor environmental input parameters for design and assessment of
energy performance of buildings - addressing indoor air quality, thermal environment,
lighting and acoustics, Brussels: CSN.
Fanger, P.O., 1970. Thermal comfort - analysis and applications in environmental
engineering, Copenhagen: Darlish Techinical Press.
Lamberts, R., Cândido, C., de Dear, R. & De Vecchi, R., 2013. Towards a brazilian standard
on thermal comfort., p.122. Available at:
http://www.labeee.ufsc.br/sites/default/files/publicacoes/relatorios_pesquisa/RP_Toward
s_a_Brazilian_Standard_Complete_Version2013.pdf.
Nicol, F. & Humphreys, M., 2010. Derivation of the equations for comfort in free-running
buildings in CEN Standard EN15251, Special Issue Section: International Symposium
on the Interaction Human and Building Environment. Building and Environment, 45(1),
pp.11–17.
Nicol, F., Humphreys, M. & Roaf, S., 2012. Adaptive thermal comfort: principles and
practice, Oxon – London: Routledge.
Peel, M.C., Finlayson, B.L. & McMahon, T.A., 2007. Updated world map of the Koppen-
Geiger climate classification. Hydrology and Earth System Sciences, 11, pp.1633–1644.
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