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Machine learning algorithms for predicting occupants' behaviour in the manual control of windows for cross-ventilation in homes

Authors
Park, JunseokJeong, BongchanChae, Young-TaeJeong, Jae-Weon
Issue Date
Oct-2021
Publisher
SAGE PUBLICATIONS LTD
Keywords
Occupant behaviour; Window opening; Cross-ventilation; Indoor air quality; Building simulation; Machine learning algorithm
Citation
INDOOR AND BUILT ENVIRONMENT, v.30, no.8, pp.1106 - 1123
Journal Title
INDOOR AND BUILT ENVIRONMENT
Volume
30
Number
8
Start Page
1106
End Page
1123
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87138
DOI
10.1177/1420326X20927070
ISSN
1420-326X
Abstract
The manual control of windows is one of the common adaptive behaviours for occupants to adjust their indoor environment in homes. The cross-ventilation by the window opening provides a useful tool to control the thermal comfort and indoor air quality in homes. The objective of this study was to develop a modelling methodology for predicting individual occupant's behaviour relating to the manual control of windows by using machine learning algorithms. The proposed six machine learning algorithms were trained by the field monitoring data of 23 sample homes. The predictive performance of the machine learning algorithms was analysed. The algorithms predicted the occupant's behaviour more precisely compared with the logistic model. Among the algorithms, K-Nearest Neighbours (KNN) shows the best fitness with the monitored data set. The driving parameters of the manual control of windows in each sample home can be clearly drawn by the algorithms. The proposed machine learning algorithms can help to understand the influence of the occupant's behaviour on the indoor environment in buildings.
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Engineering (Division of Architecture & Architectural Engineering)
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