Machine learning approach for predicting personal thermal comfort in air conditioning offices in Malaysia
The existing machine learning based models for personal thermal comfort have traditionally focused on physiological and psychological variations among occupants, and the spatial parameters have been largely overlooked. Field measurements are conducted to collect data and synthesise the collective fi...
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| Published in: | Building and environment Vol. 266; p. 112083 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2024
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| ISSN: | 0360-1323 |
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| Abstract | The existing machine learning based models for personal thermal comfort have traditionally focused on physiological and psychological variations among occupants, and the spatial parameters have been largely overlooked. Field measurements are conducted to collect data and synthesise the collective findings for optimal spatial positioning based on a 24 °C setpoint. The objective of the present study is to investigate and compare the prediction performance made by the machine learning models for personal indoor thermal comfort in air-conditioned office environments using non-spatial parameters (NSP) and spatial parameters (SP). The data was collected from the respondents at four different occupants. A comprehensive data set of NSP and SP is comprised of machine learning models in predicting different thermal comfort situations are Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Naive Bayes (NB), and Neural Networks (NN). Results indicate a substantial improvement in the accuracy prediction with the Random Forest algorithm outperforming others, enhancing overall accuracy by 38.6 % with spatial parameters for thermal sensation vote (TSV). However, the SVM algorithm improves 50 % accuracy by considering SP input for thermal comfort (TC). Spatial parameters, including the distance between windows and air conditioning units, emerge as critical factors influencing thermal comfort.
•Machine learning algorithm models are developed for personal thermal comfort.•The spatial parameters are the distance of the window and air-conditioning to the occupant.•Analysis utilising spatial and non-spatial parameters at 24 °C setpoint temperature.•Random Forest algorithm improved by 38.6 % accuracy compared to other algorithms. |
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| AbstractList | The existing machine learning based models for personal thermal comfort have traditionally focused on physiological and psychological variations among occupants, and the spatial parameters have been largely overlooked. Field measurements are conducted to collect data and synthesise the collective findings for optimal spatial positioning based on a 24 °C setpoint. The objective of the present study is to investigate and compare the prediction performance made by the machine learning models for personal indoor thermal comfort in air-conditioned office environments using non-spatial parameters (NSP) and spatial parameters (SP). The data was collected from the respondents at four different occupants. A comprehensive data set of NSP and SP is comprised of machine learning models in predicting different thermal comfort situations are Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Naive Bayes (NB), and Neural Networks (NN). Results indicate a substantial improvement in the accuracy prediction with the Random Forest algorithm outperforming others, enhancing overall accuracy by 38.6 % with spatial parameters for thermal sensation vote (TSV). However, the SVM algorithm improves 50 % accuracy by considering SP input for thermal comfort (TC). Spatial parameters, including the distance between windows and air conditioning units, emerge as critical factors influencing thermal comfort.
•Machine learning algorithm models are developed for personal thermal comfort.•The spatial parameters are the distance of the window and air-conditioning to the occupant.•Analysis utilising spatial and non-spatial parameters at 24 °C setpoint temperature.•Random Forest algorithm improved by 38.6 % accuracy compared to other algorithms. |
| ArticleNumber | 112083 |
| Author | Azizan, Azizul Othman, Nor'azizi Alam, Noor Singh, Manoj Kumar Zaki, Sheikh Ahmad Ahmad, Syafiq Asyraff |
| Author_xml | – sequence: 1 givenname: Noor orcidid: 0000-0001-6956-8567 surname: Alam fullname: Alam, Noor organization: Department of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia – sequence: 2 givenname: Sheikh Ahmad orcidid: 0000-0001-6411-9965 surname: Zaki fullname: Zaki, Sheikh Ahmad email: sheikh.kl@utm.my organization: Department of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia – sequence: 3 givenname: Syafiq Asyraff surname: Ahmad fullname: Ahmad, Syafiq Asyraff organization: Department of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia – sequence: 4 givenname: Manoj Kumar orcidid: 0000-0002-7696-846X surname: Singh fullname: Singh, Manoj Kumar organization: Energy and Sustainable Built-Environment Design (ESBD) Laboratory, Department of Civil Engineering, Shiv Nadar Institution of Eminence, Dadri, Uttar Pradesh, 201314, India – sequence: 5 givenname: Azizul orcidid: 0000-0002-5331-6071 surname: Azizan fullname: Azizan, Azizul organization: Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia – sequence: 6 givenname: Nor'azizi surname: Othman fullname: Othman, Nor'azizi organization: Department of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia |
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| Keywords | Air conditioning Support vector machines Thermal comfort Machine learning models Random forest algorithm |
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| Title | Machine learning approach for predicting personal thermal comfort in air conditioning offices in Malaysia |
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