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Expectation Classification of buildings Expectancy
Factor, e
High Non-air-conditioned buildings located in regions where air-conditioned buildings are common. Warm periods occurring briefly during the summer season. 0.9 - 1.0
Moderate Non-air-conditioned buildings located in regions with some air-conditioned buildings. Warm summer season. 0.7 - 0.9
Low Non-air-conditioned buildings located in regions with few air-conditioned buildings. Warm weather during all seasons. 0.5 - 0.7
Table 1. Expectancy factors for non-air-conditioned buildings in warm climates.
A second factor that contributes to the difference between the PMV and actual thermal sensation in non-air-conditioned buildings is the estimated activity. In many field studies in offices, the metabolic rate is estimated on the basis of a questionnaire identifying the percentage of time the person was sedentary, standing, or walking. This mechanistic approach does not acknowledge the fact that people, when feeling warm, unconsciously tend to slow down their activity. They adapt to the warm environment by decreasing their metabolic rate. The lower pace in warm environments should be acknowledged by inserting a reduced metabolic rate when calculating the PMV.
To examine these hypotheses further, data were downloaded from the database of thermal comfort field experiments. Only quality class II data obtained in non-air-conditioned buildings during the summer period in warm climates were used in the analysis. Data from four cities (Bangkok, Brisbane, Athens, and Singapore) were included, representing a total of more than 3200 sets of observations. The data from these four cities with warm climates were also used for the development of the adaptive model.
For each set of observations, recorded metabolic rates were reduced by 6.7% for every scale unit of PMV above neutral, i.e. a PMV of 1.5 corresponded to a reduction in the metabolic rate of 10%. Next, the PMV was recalculated with reduced metabolic rates using ASHRAE’s thermal comfort tool . The resulting PMV values were then adjusted for expectation by multiplication with expectancy factors estimated to be 0.9 for Brisbane, 0.7 for Athens and Singapore and 0.6 for Bangkok. As an average for each building included in the field studies, Figure 1 and Table 2 compare the observed thermal sensation with predictions using the new extended PMV model for warm climates.
Comparison of observed mean thermal sensation with predictions made using the new extension of the PMV model for non-air-conditioned buildings in warm climates. The lines are based on linear regression analysis weighted according to the number of responses obtained in each building.
City Expectancy
factor PMV adjusted to
proper activity PMV adjusted
for expectation Observed
mean vote
Bangkok 0.6 2.0 1.2 1.3
Singapore 0.7 1.2 0.8 0.7
Athens 0.7 1.0 0.7 0.7
Brisbane 0.9 0.9 0.8 0.8
Table 2. Non-air-conditioned buildings in warm climates.
Comparison of observed thermal sensation votes and predictions made using the new extension of the PMV model.
The new extension of the PMV model for non-air-conditioned buildings in warm climates predicts the actual votes well. The extension combines the best of the PMV and the adaptive model. It acknowledges the importance of expectations already accounted for by the adaptive model, while maintaining the PMV model’s classical thermal parameters that have direct impact on the human heat balance. It should also be noted that the new PMV extension predicts a higher upper temperature limit when the expectancy factor is low. People with low expectations are ready to accept a warmer indoor environment. This agrees well with the observations behind the adaptive model.