ORCID Profile
0000-0003-1302-3984
Current Organisation
University of Sydney
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Publisher: Elsevier BV
Date: 08-2022
Publisher: SAGE Publications
Date: 03-06-2020
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 in idual 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 s le 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 s le 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.
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 04-2022
Publisher: MDPI AG
Date: 18-03-2021
DOI: 10.3390/BUILDINGS11030122
Abstract: Air conditioning (A/C) is generally responsible for a significant proportion of total building energy consumption. However, occupants’ air conditioning usage patterns are often unrealistically characterised in building energy performance simulation tools, which leads to a gap between simulated and actual energy use. The objective of this study was to develop a stochastic model for predicting occupant behaviour relating to A/C cooling and heating in residential buildings located in the Subtropical Sydney region of Australia. Multivariate logistic regression was used to estimate the probability of using A/C in living rooms and bedrooms, based on a range of physical environmental (outdoor and indoor) and contextual (season, day of week, and time of day) factors observed in 42 Sydney region houses across a two-year monitoring period. The resulting models can be implemented in building energy performance simulation (BEPS) tools to more accurately predict indoor environmental conditions and energy consumption attributable to A/C operation.
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 10-2023
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 12-2021
Location: Korea, Republic of
No related grants have been discovered for Bongchan Jeong.