Recent Progress in the Adaptive Approach to Thermal Comfort

World Renewable Energy Congress VI (WREC2000) © 2000 Elsevier Science Ltd. All rights reserved. Editor: A.A.M. Sayigh

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RECENT PROGRESS 1N THE ADAPTIVE APPROACH TO THERMAL COMFORT

M.A. HUMPHREYS Oxford Centre for Sustainable Development (Architecture) Oxford Brookes University, Headington, Oxford, OX3 0BP, U.K.

ABSTRACT A large database of field studies of thermal comfort has been compiled for ASHRAE. The PMV index (ISO standard 7730) performs relatively poorly in the database, predicting comfort votes about twice the real values. The na'fve application of the standard encourages unnecessary installation and use of heating and cooling plant. The relation between the indoor comfort temperatures and the prevailing mean outdoor air temperature is similar to that derived from an earlier database. These results could help provide an adaptive comfort standard in a revision of ISO 7730. Attention is drawn to some other advances in thermal comfort field research.

KEYWORDS Thermal comfort; field studies; ASHRAE database; ISO 7730.

BACKGROUND The adaptive approach to thermal comfort rests on people's tendency to make themselves comfortable. They alter their environment to suit themselves. They turn the heating up or down, open or close windows, raise or lower blinds, or turn fans on and off. They adjust themselves to suit the environment. They increase or reduce their clothing, change their degree of activity, or alter their posture. They seek a more comfortable place. They move to a warmer spot in a room, or move out of direct sun, or sit in a cool breeze near a window. They choose different rooms in a house at different times of the day or for different seasons. These and other actions can be summed up in an 'adaptive principle': I f a change occurs that produces discomfort, people will tend to act to restore their comfort. The adaptive approach starts with human behaviour. It explores the dynamic relation between people and their surroundings. It is more likely to observe people in their everyday environment than experiment on them in a laboratory. Field studies of thermal comfort have found that people are capable of achieving comfort in a wider range of thermal environments than would have been expected from calculations based on human physiology and heat transfer. ISO Standard 7730 for the thermal environment (ISO, 1994) rests on such calculations. It is essentially Fanger's PMV equation. The application of the standard excludes thermal environments that in practice are comfortable. Too restrictive a standard needs more heating or cooling plant, and uses more energy for its

439 implementation. Capital and running costs are high, and, unless the energy comes from renewable sources, more carbon dioxide reaches the atmosphere. The adaptive approach suggests that comfort may often be achieved by the careful design and operation of buildings, without resort to air-conditioning. Such buildings do not at all times meet ISO 7730, but the occupants like them nonetheless. It would be good to have an adaptively based standard for use in such buildings, and there are proposals to incorporate an adaptive element in the current revision of ISO 7730. In the context of this proposed revision it is timely to examine afresh the relation between the thermal sensations predicted by ISO 7730 and those experienced in daily life.

A DATABASE OF THERMAL COMFORT FIELD-STUDIES A database of field studies from some 160 buildings in various countries and climates has been assembled for the American Society of Heating, Ventilating, Refrigerating and Air-Conditioning Engineers (ASHRAE) (de Dear 1998, de Dear and Brager 1998). The data come from the climates of North America, Europe, Asia and Australia. They consist of over 20,000 individual observations of thermal comfort, mostly from office workers, with concurrent measurements of their thermal environments. Appended to the observations are calculated values of some common indices of thermal comfort, including the Operative Temperature and the Predicted Mean Vote (PMV). Three types of building are distinguished: centrally air-conditioned, mixed mode, and naturally ventilated. The "comfort votes' are responses on the ASHRAE scale of subjective warmth [cold(-3), coo1(-2), slightly cool(-1), neutral(0), slightly warm(I), warm(2), hot(3)], or on similar seven-category scales. The database is an important addition to our information on thermal comfort in the circumstances of daily life. How well does ISO 7730 (PMV) predict the actual 'comfort votes' of the occupants of these various buildings?

Individual votes

The effectiveness of an index of thermal comfort is its ability to explain the diversity of the comfort votes. The criterion is the square of the correlation coefficient (r2) between the values of the index and the values of the corresponding individual votes. It is the proportion of the variance of the comfort vote explained by the variance of the index. Operative temperature (as defined in the database) is the mean of air and radiant temperature. It is a simple measure, almost indistinguishable from the temperature of a globe thermometer. It explains 29% of the variance of the votes. PMV adds to the globe temperature allowances for air speed, humidity, clothing insulation and metabolic rate, combining them by means of a complex formula. It explains 23% of the variance. Both indices leave a large proportion of the variance unexplained. This proportion, (1- r2), comprises the differences between people, and the inconsistency of individuals. Such unexplained variance is usual in thermal comfort studies, and efforts to explain it by means of other factors have had little success. The Operative temperature is better than PMV. Increasing the completeness of the description of the thermal environment, and incorporating changes in clothing and activity, have decreased the predictive power of PMV. This counter-intuitive finding is not new, but its recurrence in the ASHRAE database puts it beyond reasonable doubt. A better prediction of an individual comfort vote can be made from Operative Temperature than from PMV. Why is this so? The metabolic rate, the clothing insulation, and the air movement are difficult to measure accurately, so PMV is contaminated by measurement error. Also, approximations in the formulation of PMV produce scatter in its values (formulaic error). The effect of these errors is illustrated in figure 1. The trend-line is the regression of the comfort vote on PMV, and is therefore the estimate of the actual mean vote for any particular value of PMV. This line should pass through the origin and have unit gradient, for the predicted mean vote (PMV) should always equal the actual mean vote. The combined effect of measurement and formulaic error is large, for the gradient is only about half the expected value. The implication of this finding is clear. If

440

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Eliminating the measurement error

There are circumstances where measurement error is not relevant. For example, PMV may be used to assess the comfort of an environment predicted by the thermal simulation of a room, using CFD or other methods. Contours of PMV are drawn, using the estimated surface temperatures, airflow patterns, and air-temperature gradients. Alternatively, it may be desired to assess the likelihood of discomfort in some other hypothetical environment. If the problem with PMV is solely attributable to measurement error, the predictions will be sound. If, however, PMV contains non-trivial formulaic error, the predictions will be misleading. It is therefore necessary to disentangle formulaic error from measurement error. The effects may be disentangled by considering the group mean vote and group mean PMV for the data from each of the 160 buildings in the database. For buildings having 100 or more observations, the standard error of the group-mean is one tenth or less than that of an individual observation, and may be disregarded for practical purposes. This procedure largely eliminates the effect of the random error in the measurement of PMV, and of scatter in the subjective response. Any remaining error is therefore attributable almost entirely to formulaic error in the PMV equation, and to any systematic error in its estimation. Figure 2 is a scatter plot of the group mean vote and the group mean PMV for these buildings. Had PMV been free from formulaic error, each point would lie very close to a line of unit slope and zero intercept. Both the correlation coefficient and the regression gradient would approach unity. In fact r 2 is 0.6 and the gradient is 0.5. These values show that considerable error remains. Its size can be estimated either from the depression of the gradient of the regression line, or from the difference of r 2 from unity (Humphreys & Nicol 2000a). Calculation shows that formulaic sources have contributed an error of standard deviation some 0.6 ASHRAE scale units to

441 the estimates of the mean values of PMV. The overall standard deviation of the group means of PMV is 0.8 scale units, so the formulale error is a large proportion of the range of PMV. The consequence of this formulaic error is that PMV on average predicts a group mean vote about twice the actual value. It so happens that the formulaic error is related to the room temperature. Figure 3 shows the error in PMV in relation to the group-mean Operative temperatures. The scatter plot shows both PMV and the actual mean vote (ASH). The error is such as to encourage unnecessary cooling in warm conditions, and unnecessary heating in cool conditions. The bias (PMV-ASH) in terms of the Operative temperature (To) is given by the equation: (1"2=0.55, t=9, p<<0.001)

(PMV-ASH) = 0.13To- 3.2

This confirms a similar result based on earlier, less comprehensive data (Humphreys 1992). If PMV is used, a correction must be applied. Failure to do so will produce misleading estimates of the mean subjective warmth of occupants of buildings whose normal temperatures differ from those that have become customary in heated or cooled buildings in the developed world.

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Comfort temperatures in relation to outdoor temperature The database enables a fresh estimate to be made of the relationship between the temperatures found to be comfortable indoors and the prevailing mean outdoor temperature. Figure 4 compares the relation obtained from earlier surveys with a similar relation from the new database. To enable comparison, both sets of data have been related to the mean outdoor air temperature. The neutral temperatures from buildings in the freerunning mode are separated from those from buildings in the heated or cooled mode. A correction has also been applied to the neutral temperatures, from both databases, to allow for the influence of formulaic error on the estimates. Humphreys and Nicol (2000a, 2000b) explain the methods. The lines therefore differ to some extent from those of Humphreys (1978), and from those of de Dear and Brager (1998). The relationship for the freerunning buildings is virtually unchanged by the passage of years. The neutral temperatures in heated or cooled buildings have drifted with time, although the form of the relation is similar. These graphs offer a practical alternative to the use of ISO 7730, and apply to lightly active people clothed to suit themselves.

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Fig. 4. Relation of indoor neutral temperatures to the prevailing outdoor mean air temperature. The left figure is from the ASHRAE database, the right from Humphreys' 1978 database. Filled points are for the free-running mode, open points for the heated or cooled mode. Trend lines are by Lowess fitting (80%, 3 iterations).

SOME OTHER DEVELOPMENTS I have given considerable space to the ASHRAE database, but that should not preclude the mention of other interesting developments. New field surveys. It is good to have indigenous results from Tunisia (Bouden et al 1998), the U.K. (Oseland 1998), Zambia (Malama 1997, 1998), Hong Kong (Chan et al 1998) and from Hawaii (Kwok 1998). These all help to fill gaps in the world-wide map of comfort temperatures. There is also an interesting study by from Iran (Heidari et al 2000), exploring the comfort-seeking activity of people within their dwellings. Thermal models. Rapid computation makes practical more complete models of the human physiological responses for everyday application. An example that treats the transient responses quite fully is that of Fiala (1998). It will be interesting to discover whether these models are capable of improving the prediction of thermal comfort in everyday circumstances. Intermediate spaces. Some spaces are neither fully indoors nor fully outdoors. There have been thermal comfort studies in such spaces, both for brief occupancy (J 199 ) and for more prolonged occupancy. Of particular interest are studies of covered shopping malls in Japan. It appears that people assess such spaces differently from indoor spaces (Chun 1999, Jitckhajornwanich et al 1998). Outdoor spaces. There is a revival of interest in thermal comfort outdoors, chiefly in relation to the leisure use of outdoor urban spaces (Nicolopoulou 1999). Again it seems that people apply different criteria when evaluating their thermal sensations outdoors.

Semantic offset. It was suggested many years ago that the understanding of the ASHRAE scale might be affected by the season of the year, or by the outdoor temperature. People might prefer to feel slightly warm in winter, and slightly cool in summer. De Dear and Brager (1998) found that, in the ASHRAE database, there was a small but significant seasonal change in this direction for people in air-conditioned buildings, but not for people in naturally ventilated buildings. This is a puzzling result, and suggests that we do not yet understand the

443 effect of context on the way people respond to thermal comfort scales. It may therefore be timely to revisit this question, so that improvements in the specification of the thermal environment can be matched by improvements in the methodology of its subjective evaluation.

Time and thermal comfort. The adaptive approach to thermal comfort is essentially dynamic, yet thermal comfort research on variations associated with time is sparse. J F Nicol treats this important topic in his paper to this conference. REFERENCES Bouden, C, N. Ghrab-Morcos, F. Nicol and M. Humphreys (1998). A thermal comfort survey in Tunisia. EPIC 98, Lyon, France. Proceedings ACTES, 491-496. Chan, D.W.T., J. Burnett, R.J. de Dear and SCH Ng (1998). A large-scale survey of thermal comfort in office premises in Hong Kong. ASHRAE Transactions, 104(1). Chun, C and A Tamura (1999). Human thermal votes with temperature change while walking. Proceedings of 8 th international conference on indoor air quality, 1, 661-666, Edinburgh, Scotland. de Dear, R.J. 1998. A global database of thermal comfort field experiments. In: FieM Studies of thermal comfort and adaptation. ASHRAE Technical Data Bulletin, Vol. 14(1), pp. 15-26, ASHRAE, Atlanta, USA. (Also in ASHRAE Transactions, 104(1), 1998). de Dear, R.J. and G.S. Brager 1998. Developing an adaptive model of thermal comfort and preference. In: FieM Studies of thermal comfortand adaptation. ASHRAE Technical Data Bulletin, Vol. 14(1), pp. 27-49. (Also in ASHRAE Transactions, 104(1), 1998). Fiala, D. (1998). Dynamic simulation of human heat transfer and thermal comfort. PhD Thesis, de Montford University, Leicester, U.K. Heidari, S, A.C. Pitts and S. Sharpies (2000). Adaptive comfort behaviour in Iranian courtyard houses. Proceedings WREC Congress, Brighton. Humphreys, M.A. (1978). Outdoor temperatures and comfort indoors. Building Research and Practice (d. CIB), 6(2), 92-105. Humphreys, M.A. (1992). Thermal Comfort requirements, climate and energy. 1725-1734 in: Renewable Energy, Technology and the Environment. Ed: A A M Sayigh, Pergamon. Humphreys, M.A. and J.F. Nieol (2000a). Effects of measurement and formulation error on thermal comfort indices in the ASHRAE database of field studies. ASHRAE Transactions 106(2). Humphreys, M.A. and J.F. Nicol (2000b). Outdoor temperature and indoor thermal comfort - raising the precision of the relationship for the 1998 ASHRAE database of field studies. ASHRAE Transactions 106(2). ISO (1994). Moderate thermal environments- Determination of the PMV and PPD indices and specification of the conditions for thermal comfort. ISO 7730, International Organization for Standardization, Geneva 20, Switzerland. Jitkhajornwanich, K., A.C. Pitts, A. Malama and S. Sharples (1998). Thermal comfort in transitional spaces in the cool season of Bangkok. ASHRAE Transactions 104(1). Kwok, A.G. (1998) Thermal comfort in tropical classrooms. ASHRAE Transactions 104(1). Malarna, A. and S. Sharples (1997). Thermal performance of traditional and contempory housing in the cool season of Zambia. Building and Environment, 32(1), 69-78 Malama, A., S. Sharpies, A.C. Pitts and Jitkhajornwanich K. (1998). An investigation of the thermal comfort adaptive model in a tropical upland climate. ASHRAE Transactions 104(1). Nicolopoulou, M., N. Baker and K. Steemers (1998). Thermal comfort in outdoor urban spaces. Environmentally friendly cities, Proceedings of 15th PLEA Conference, Lisbon, James& James. Oseland, N.A. (1998). Acceptable temperature ranges in naturally ventilated and air-conditioned offices. ASHRAE Transactions 104(1). Sharpies, S. and A. Malarna (1997) A thermal comfort field survey in the cool season of Zambia. Building and environment, 32(3), 237-243.