Abstract

Previous studies have developed plenty of mathematical models for assessing indoor environment quality. Literature reviews that most of them presented a mean value of perception of a population and ignored the interaction effects of multiple environmental factors on occupants' perceptions. In this regard, the aim of our paper is to develop a comprehensive indoor physical environment quality (IPEQ) index which could present a whole picture of perception of a population by taking the combined effects (i.e., main, crossed and interaction effects) into consideration. The hypothesis and methodology of quantitation were firstly proposed according to Probability Theory. Our quantitation was performed based on the subjective perception votes that collected from 216 experimental conditions in a controlled laboratory. Three 3-input 7-output Artificial Neural Network models were subsequently developed for predicting probability mass functions (PMFs) of 7-point sensation votes under the combined effects of temperature, illuminance, and sound level. Thereafter, the PMFs of 3-point satisfaction votes were expressed and calculated by using Law of Total Probability. Finally, a comprehensive IPEQ index-Predicted Probability Overall Dissatisfied (PPOD) index, which provides an estimate of how many occupants in a space would feel dissatisfied with the overall physical environment, was developed. The predicted expected value of overall satisfaction distribution (average overall satisfaction) was validated to be roughly in accordance with previous studies and more reasonable when at least one kind of environmental factor was far away from expectation. The new quantitative measure also indicated that the minimum PPOD is 17.55% (air temperature=24.3°C, illuminance=910Lx, sound level=35dB) within common ranges of environmental parameters. It was also discussed that our new quantitative measure could provide quick and direct suggestions for controlling or designing the indoor environment.

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