Comfort criteria for passively cooled buildings a pascool task

Renewab/e Energy, Vol.5, Part IL pp. 977.--984,1994 Elsevier Science l.,td Printed in Ca-eatBritain 0960-1481/94 $7.004.0.00

Pergamon

COMFORT CRITERIA FOR PASSIVELY COOLED BUILDINGS A PASCOOL TASK

Nick Baker Mark Standeven The Martin Centre for Architectural and Urban Studies Department of Architecture University of Cambridge Cambridge CB2 2EB U.K.

ABSTRACT The application of passive cooling techniques to buildings in warm climates creates the need for appropriate comfort criteria. Conventional comfort criteria, usually based upon laboratory experiments, seem to be unnecessarily severe. The paper describes preliminary findings of the PASCOOL Thermal Comfort task which, responding to renewed interest in behavioural aspets of thermal comfort, sets out to establish appropriate limits by field studies and theoretical considerations.

KEYWORDS Adaptive behaviour, comfort limits, environmental monitoring, passive cooling, thermal comfort.

INTRODUCTION Nearly half the world's energy use is associated with providing environmental conditioning in buildings and about two thirds of this is for heating, cooling and mechanical ventilation. Whilst in cooler climates, the energy used for heating has been reduced by the application of conservation technologies, energy requirements for cooling are on the increase. In Greece, for example, the greatest electrical demand now falls in the summer, in response to the ever increasing demand from air-conditioning in both non-domestic and domestic buildings. The perceived need for mechanical cooling is to achieve accepted standards of thermal comfort, usually defined (directly or indirectly) by temperature limits. There is, however, growing controversy as to what these standards are. For some time it has been observed that there has been an apparent discrepancy between comfort predictions using models derived from laboratory experiments such as those by Fanger (1970) and subjective assessments of comfort found in field studies. For example, in a compilation of results from 47 field studies, predominantly in warm and hot climates, Humphreys (1978) found that the preferred comfort temperature in buildings was a function of the average monthly outdoor temperature Tn = 0.534 To + 11.9

(1)

where To is the mean monthly temperature.

Fanger's theory relates the sensation of hot or cold (Predicted Mean Vote, PMV), and subsequently the discomfort or dissatisfaction (Predicted Percentage Dissatisfied, PPD), to the imbalance between the heat produced by the bodies metabolism, and the heat loss to the environment. Obviously this imbalance cannot exist indefinitely, and the sensation of discomfort is a signal to the person to take some action to restore heat balance. s:8-o

977

978

(2)

PMV = (0.303 e ".036 M +0.028 ) ( M - H ) where M is the metabolic rate and H is the heat loss to the environment.

(3)

PPD = 100 - 95 exp -(0.0335 PMV 4 + 0.218 PMV 2)

The result of using Fangcr's equations seems to predict the need for much more closely controlled conditions than one usually finds in free-running buildings, in which people still seem to be comfortable. For example the ISO 7730, based upon Fangcr's equations, recommends an optimal operative temperature of 24.5 oC +/1.5oc for light sedentary work with light summer clothing. But in our field study in Athens (table 1) we found that typically 70% of subjects were satisfied at an operative temperature of 27.8oc, and on one site in particular, 89% of subjects were satisfied at operative temperatures as high as 30.5oc. Table 1 Summary of questionnaire responses to subjective feelings of comfort during office hours. Survey Expl Exp2 Exp3 ExlM Exp5 Exp6 Exp7 MeanOperativeTemp. 27.7 32.1 30..5 Total Hours 175 97 83 Neutral Sensation % 40 12 25 ComfortableSensation* 83 77 90 No ChangePreference% 63 21 66 ThermalSatisfaction**% 71 44 89 * - Comfortablesensationis definedas warm,neutralor cool. ** - Thermalsatisfactionis an integratedresponsefor the previoushour.

29.1 119 38 84 57 83

27.7 120 25 78 52 67

26.5 103 29 85 68 81

N/A 167 30

77 63 76

Two questions then arise - firstly, arc more liberal criteria really justified, or is it simply disguising the acceptance of lower standards, and secondly how can these new limits be established. The first question seems to have been partly answered, in the affLrmative, by the many field studies that have been reported. To answer the second, we can identify two approaches. One is to simply use the statistical data from field studies to establish an empirical expression, which indeed Humphreys (1978), and Aliciems (1977) have already done. However, this fails to identify the key factors, and the scatter of field data, cleaxly indicates that not all buildings are equal in their provision of thermal comfort. The second approach, is to attempt to develop a mechanistic model which explains the apparent discrepancy. This would identify those factors which may facilitate and maximise the achievement of thermal comfort. Some o f these factors may be in the designers control, and some in the control of the occupant. This latter approach has been that taken in the Thermal Comfort task in the PASCOOL programme (Baker, 1993). The overall aim of the programme is the promotion of passive cooling in buildings (Santamouris and Argiriou, 1993).

THE P A S C O O L T H E R M A L C O M F O R T T A S K Various explanations have been given for the apparent discrepancy between observed comfort conditions, i.e. conditions in which the occupants express satisfaction, and those predicted by conventional theory. Most of these explanations involve the concept of adaptive changes made by the occupant, such as operating building controls, seeking out the most comfortable part of the room, and adjusting clothing, posture and metabolic rate. This paper describes the PASCOOL task to investigate these adaptive processes, to quantify them, and relate the opportunities for adaptation to building design, where relevant. Ultimately the goal is to incorporate the findings into a predictive comfort model which is appropriate to passively cooled buildings.

979 To obtain information about the thermal experience of real building occupants, a survey methodology has been developed which simultaneously records room environmental conditions, the local thermal experience of an individual and various subjective feelings of comfort. The room conditions are recorded using a conventional datalogger and sensors; air temperatures, globe temperatures, surface temperatures and air movement are logged every 15 minutes on a total of sixteen channels. The occupants wear a sensor array mounted on a Walkman-type headset attached to a data-recorder, worn on a waist belt. Air temperature, air velocity, and two globe temperatures (one either side of the head) are monitored. In the PASCOOL studies, subjects agreed to wear the arrays at home and on the way to and from work, for the four day monitoring period. The subjective thermal response and preference together with a range of recent actions are recorded hourly using paper questionnaires. The first surveys were made during the summer months of 1993 using one building in Lyon and six buildings in Athens with a total of twenty three subjects. From the experience of this f'zrst study the headset arrays have been improved. Uncertainties about actual local conditions due to the interaction of the subject's heat loss plume have been quantified and the appearance and convenience of the headsets have been refined. More technical details of the monitoring methodology are given elsewhere (Standeven and Baker, 1994b). The emphasise of the second series of studies in 1994 will be to explore more closely the difference between measured room conditions and individual local conditions, and to identify building characteristcs which improve the probability of occupants being comfortable.

COMFORT PREDICTION AND "ADAPTIVE ERRORS" First, it must be made clear, that the critical issue is when comfort criteria are applied to the predicted thermal conditions in proposed buildings. It is at this stage that inappropriate comfort criteria may lead the designer into adopting the high energy air-conditioned path. Once a building is built, the issue of comfort criteria becomes less critical, since the ultimate judgement can now be made by the occupants. Figure 1 shows the essential steps in this predictive process and it will be used to form the basic structure of this paper. In the first column (rectangular boxes) is the main sequence of inputs and outputs. The next column (ellipses) shows the source of "errors" which may result in a cumulative error in the final result. In the right hand column, we show the experimental procedure for investigating and quantifying these "errors". We will now discuss these steps in more detail.

J Buildingand Climate I

I I Room ConditionsJ I:

~upant

Interact~ns~'~ ~

room monitoring

-,4-. personalmonitoring

I'oca,ond,,onsl

]

observation & questionnaire

I'!erma'SensatiooI I

~P"~chological factors~'~ ~

questionnaire

IThermalSatisfactionJ Figure 1 The discrepancy between predicted thermal satisfaction and observations.

980

Room conditions The first step is the prediction of room conditions from climatic and building data. This is now a routine activity for thermal modellers, but is not without error as various blind comparisons between modellers and models testify. However, random error would lead to more favourable conditions being predicted with the same frequency as less favourable conditions. But error which is due to the failure to estimate the effect of occupant interactions, may tend to underestimate the beneficial effect since modellers are wary of assumptions such as "the occupants will deploy the shading devices during times of overheating". One thing that can be certain, is that in real life occupant interactions will be biased towards improving the conditions. People will not go into a room, complain of the heat, and then raise the blinds and sit in the sun. Similarly, it is inconceivable that a person feeling uncomfortably cold would, in a cool climate, open the window. Thus we see that inadequate estimation of occupant interaction, is likely to underestimate the beneficial effect of occupants on room conditions. Modellers lack data in this area. In the PASCOOL task we have investigated this in two ways. Firstly the hourly questionnaire requests information such as "have you made

adjustments in the last hour to furniture, doors, windows, shades, fans or any other part of the building to improve your comfort?" Secondly the observer, present in the main monitored room during the survey, records any activities made by the occupants. A third approach will be made in the monitoring campaign of summer 94, using an automatic wide angle camera taking photographs approximately every 6 minutes. Results from the hourly questionnaires in the 1993 surveys indicate plenty of occupant interaction. Table 2 below shows the responses for all seven surveys. For a total of 864 hours between 9am and 6pro (office hours) there were 273 adjustments to controls or other environmental aspects. Table 2 Total number of occupant interactions recorded from the questionnaires in each survey. Survey Expl Exp2 Exp3 Exp4 Exp5 Exp6 Exp7 BuildingLocation

Athens

Athens

Athens Athens

Athens

Athens Lyons

Total Hours Mean RoomAir Temp. Numberof RoomChanges Numberof Clothin~Changes

175 27.6 38 8

97 32.1 60 19

83 30.4 24 I0

120 27.4 62 7

103 25.5 18 0

119 28.7 43 9

167 N/A 28 9

Comparison between predicted room conditions and measured conditions has been inconclusive, mainly due to the failure to obtain a truly blind prediction of the existing building from the model. Furthermore the adaptive effects may be quite small, and can be masked by random modelling errors. This aspect of the study will be an important objective of the 1994 monitoring campaign. It is obvious that different buildings offer varying degrees of opportunity for these kinds of adaptive interventions by the occupant. This introduces the concept of adaptive opportunities, and the degree to which these are present being a key factor in occupant satisfaction. Many anecdotal accounts of occupant dissatisfaction in large closely controlled offices, where the occupant has no access to controls of any kind, supports this idea. Access to building controls is just one aspect of adaptive opportunity; we will return to this concept shortly. Local conditions The next step to predict a local temperature, is in practice, rarely taken. Modellers usually predict a single characteristic room temperature (this may be air, mean radiant or some environmental temperature) to describe the whole room. However, in certain thermal regimes, conditions are highly non-uniform. For example, in cool climates, considerable temperature gradients exist between the heat emitters and sources of

981

heat loss such as windows. More relevant to overheated situations is the high radiant temperatures in or near to sun patches, and the lower effective temperature, in naturally induced airstreams. Common sense tells us that occupants will tend to use these variations to maximise their comfort. We would not expect anyone who was feeling cold to move further away from the trtre! Similarly, the conscious and unconscious migration to cooler parts of the room, moving into air streams or into contact with cooler surfaces is quite compatible with everyday experience. Clearly this is a another component of adaptive opportunity. Most thermal models are not equipped to deal with this due to their single point room output. Baker and Newsham (1989), using a simulation model that mapped radiant and air temperature in a room for a 3x3x3 array of cells, showed that by allowing the "occupant" to move to the most comfortable position at every hour time step, for a period during the overheated summer months, overheating as defined by a Predicted Persons Dissatisfied > 20% was reduced from 530 hours to 115 hours. Although a rather artificial situation assuming total mobility - the interesting outcome is the very large reduction in overheating hours suggesting that the overall thermal satisfaction may be quite sensitive to small differences in adaptive opportunity. We have tried to detect this in our comfort monitoring using data from the personal sensor arrays. These recorded the actual thermal environment experienced by the subject, for comparison with the room environment as measured. A comparison of the difference between local conditions and a room average consistently shows lower local temperatures. Table 3 below shows the mean air and radiant temperatures as recorded for the room and the individuals together with the differences. Table 3 Mean room and local temperatures for all sub~ects in each 1993 survey. Survey

Expl

Exp2

Exp3

Exp4

Exp5

Exp6

Exp7

Building l.,ocation

Athens

Athens

Athens

Athens

Athens

Athens

Lyons

Mean Room Air Temp.

27.6

32.1

30.4

28.7

27.4

25.5

*

Mean Local Air Temp.

27.1

30.8

29.9

28.6

28.1

27.2

25.6

Difference

-0.5

- 1.3

-0.5

-0.1

0.7

1.7

*

Mean Room MRT

27.9

32.3

30.6

29.7

28.3

28.6

*

Mean Local MRT

26.6

31.2

29.9

28.6

27.6

26.4

26.2

Difference

- 1.3

- 1.1

-0.7

- 1.1

-0.7

-2.2

*

* - The room eonditiens in Lyons were missed.

Results show, that for air temperature alone, occupants typically experience 0.5 - 1.5 oC lower than the room average temperature. Using standard statistical techniques the difference between the mean room and local air temperatures in each survey is significant at better than the 2% level for all except Exp4. The mean room and local MRTs are all significantly different at better than the 1% level. Figure 2 shows a typical one-day history of a subject showing the decrement of average local temperature below room temperature. It also shows that the subject experiences a thermal texture with an amplitude of about 3oc, interesting but as yet of unknown significance.

982 0 32.00. o 30.00.

P 28.00. ,,,I

'~ 26.00no 24.00. oE 22.00. I- 20.00

Time (hr) room air

local air

Figure 2 A one-day thermal history of a subject during working hours In all seven field trials, personal temperatures tended to be lower than room temperatures. A major difficulty in this comparison, is to measure a true room average. In particular, air movement which spatially, is highly non-uniform and for which there was only one anemometer sensor available. Particular attention will be paid to this problem in the 1994 campaign. Personal parameters Clothing So far we have been discussing the objective parameters which control heat balance and, as hypothesised by conventional theory, thermal comfort. Heat balance is also controlled by personal parameters - Clothing and posture will influence heat loss, and metabolic rate is the controlling parameter of heat gain. We shall now show that these parameters are also in the category of adaptive opportunity. In conventional comfort calculations "standard" values of clothing insulation will be applied using simple descriptions to identify these values from tables. For example an ensemble of briefs, long light-weight trousers, open-neck shirt with short sleeves, light socks and shoes, will be ascribed (Int. Std. ISO 7730) a Clo value of 0.5. However, common experience tells us that there has been some rounding-up here, and factors such as closeness of fit, or whether cotton or polyester, have not been accounted for. Considering the differences in processes such as "pumping" ventilation between the skin and cloth, and the relative absorbencies of cotton and polyester, it we would expect a significant sensitivity to these factors. The thermal performance of clothing has been studied in some detail by Berger (1988, 1993), and he points out that in some cases the effective insulation value may even be negative, i.e. the clothing is promoting heat loss by the mechanisms described above, rather than inhibiting it. He also points out that thermal sensation may not be the limiting factor for hot discomfort, but rather skin wettedness. This results in conventional predictions being pessimistic in conditions of low humidity and good air movement, but optimistic at high humidities. In the PASCOOL task we have not investigated clothing insulation values directly, but have looked for evidence that subjects make adjustment to their clothing in order to maximise comfort. This is mainly determined from the hourly questionnaire in response to the question "have you adjusted, removed or added any of your clothing in the last hour?", and also from reports by the observer. The proposed time lapse photography will also provide data. However, we cannot so far make quantitative evaluations of clothing insulation value changes.

983 Results from the 1993 monitoring (table 1) show that in a total of 864 observed hours in the main workplace, amongst 28 subjects, there were 62 adjustments to clothing level. This suggests that clothing level and state are adaptive opportunities. Unlike building controls, they are not in the control of the architect, but rather, particularly in the workplace, the constraints of convention and rules of the management. Posture influences the effective area of the body and clothing surface exposed to convective and radiant heat loss. It is common experience that people adopt a more extended posture when warm or hot, and a more compact pos~re when cold. Preliminary calculations suggest that the freely ventilated surface can vary by as much as 20% between these two conditions 1. Posture will also influence the ventilation of clothing, thus having an indirect effect on clothing insulation value. Quantitative observation of posture is not straightforward, and has not been carried out so far in the PASCOOL study. Time-lapse photography will be used to furnish data in the 1994 field study. Metabolic rate Activity and metabolic rate result in heat production and hence influence heat balance. The possibility that metabolic rate is an adaptive opportunity and hence a source of adaptive error in thermal comfort is explored elsewhere in some detail by the authors (1994a). There it is proposed that metabolic rate reductions of at least 10%, for nominally the same task, in order to avoid hot discomfort, are quite plausible. Metabolic rate monitoring by heart-rate monitoring or by accelerometery, is also discussed.

EFFECT OF "ADAPTIVE ERRORS" We have now identified a number of "adaptive errors" and made preliminary estimates of their magnitude. Note that these are not random errors, but are biased towards improving comfort. We can now measure their impact on comfort if applied to a comfort calculation The result is shown below in table 4 Table 4 Effect of adaptive errors base case

adapted case

room temp air room temp rad local temp air local temp rad air speed clothing (clo) Activity (met)

30.5 30.5 30.5 30.5 0.1 0.5 1.2

29.5 29.5 28 28 0.2 0.4 1.1

(29) (29) (0.1) (0.5) (1.1)

predicted % dissatisfied 68.4

17.5

(35.7)

Two sets of results are shown. The f'trst are optimistic, but not excessively so. It is assumed that the room air and radiant temperature is actually loC lower due to occupant interactions, than predicted. A further 1.5°C reduction for local temperature is based on observations from the PASCOOL survey. A local increase of airspeed from 0.1 to 0.2 is assumed. A 20% reduction on clothing insulation value includes posture change, and finally a 10% reduction in metabolic rate is applied. These parameters are input for the Fanger equations used to calculate predicted % dissatisfied (PPD). The results show a dramatic reduction in PPD from 68.4% to 17.5%. Even with the pessimistic case, with no room error, no windspeed error and no clothing error, (values in parenthesis), the PPD is reduced to 37.5%.

1This is calculated from the change of exposed surface area when the arms are held away from the body and the legs apart.

984

CONCLUSION The ideas suggested in this paper are only incompletely supported by measured data. The important message is however, that a number of small effects which in themselves seem plausible, could add up to a very significant effect, explaining the apparent discrepancy between objectively calculated responses and those observed. It also shows that it may not be an issue of whether objective heat balance models, such as that of Fanger, are fight or wrong. It is difficult to argue with the basic physics of the model and the laboratory assessment of subjective responses is, no doubt, equally valid. What is more the point of debate is the validity of the data that goes into the calculation. We have proposed the term adaptive errors, the main characteristic of these being that they are not random, and always biased towards minimising discomfort. This is only giving a name to a concept suggested by several other workers (Humphreys, 1978; Nicol, 1973) over the last two decades, and more recently in a renewed interest in the topic, for example (Nicol, 1992). We have also described adaptive opportunity which in effect allows the neutral zone to be extended, and that adaptive opportunity is partly an attribute of the building, and partly of the social conditions in the building. It may be that assessing the adaptive opportunity in a building, together with its passive climatic response, is a more appropriate way of judging a proposed building, than applying fixed temperature standards to predicted data of doubtful validity. REFERENCES Auliciems, A. (1977).Thermal comfort criteria for indoor design temperature in the Australian winter. Arch Sci Rev. Dec 86-90 Baker, N .V. (1993). Thermal comfort evaluation for passive cooling - A PASCOOL task. Proc Conf. Solar Energy in Architecture and Planning, Florence. H S Stephens & Associates. Baker, N.V. and Newsham, G. (1989). Comfort studies by spatial modelling of a direct gain room. Proc. 2nd European Conference on Architecture, Paris. Kluwer. Baker, N.V. and Standeven, M.A. (1994a). Thermal comfort criteria in free-running buildings. Proc. PLEA 11th International Conference, Dead Sea, Israel. Berger, X. (1988). The pumping effect of clothing. Int J Ambient Energy, 1. Berger, X. (1993). About thermal comfort. Paper presented at the3rd PASCOOL meeting, Florence. Fanger, P.O. (1970). Thermal Comfort: Analysis and Application in Environmental Engineering.Danish Technical Press. Humphreys, M (1978). Outdoor temperatures and comfort indoors. Building Research and Practice. 6(2). Nicol, J.F.(1973). An analysis of some observations of thermal comfort in Roorkee, India and Baghdad, Iraq. Annals of Human Biology 1 (4) 411-426. Nicol, J.F.(1992). Passive buildings need active occupants.Proc.WorldRenewable Energy Congress. Santamouris, M. and Argiriou, A. (1993). The CEC project PASCOOL. Proc Conf. Solar Energy in Architecture and Planning, Florence. H S Stephens & Associates. Standeven, M.A. and Baker, N .V. (1994b). A Personal sensor array for environmental monitoring in the PASCOOL Comfort Field Surveys, Internal paper presented at the 7th PASCOOL meetinl~, La Rochelle.