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.

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