{"title":"预测马来西亚空调办公室个人热舒适度的机器学习方法","authors":"","doi":"10.1016/j.buildenv.2024.112083","DOIUrl":null,"url":null,"abstract":"<div><div>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 (<em>NSP</em>) and spatial parameters (<em>SP</em>). The data was collected from the respondents at four different occupants. A comprehensive data set of <em>NSP</em> and <em>SP</em> is comprised of machine learning models in predicting different thermal comfort situations are Decision Trees (<em>DT</em>), Random Forests (<em>RF</em>), Support Vector Machines (<em>SVM</em>), K-Nearest Neighbours (<em>KNN</em>), Naive Bayes (<em>NB</em>), and Neural Networks (<em>NN</em>). 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 (<em>TSV</em>). However, the <em>SVM</em> algorithm improves 50 % accuracy by considering <em>SP</em> input for thermal comfort (<em>TC</em>). Spatial parameters, including the distance between windows and air conditioning units, emerge as critical factors influencing thermal comfort.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning approach for predicting personal thermal comfort in air conditioning offices in Malaysia\",\"authors\":\"\",\"doi\":\"10.1016/j.buildenv.2024.112083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 (<em>NSP</em>) and spatial parameters (<em>SP</em>). The data was collected from the respondents at four different occupants. A comprehensive data set of <em>NSP</em> and <em>SP</em> is comprised of machine learning models in predicting different thermal comfort situations are Decision Trees (<em>DT</em>), Random Forests (<em>RF</em>), Support Vector Machines (<em>SVM</em>), K-Nearest Neighbours (<em>KNN</em>), Naive Bayes (<em>NB</em>), and Neural Networks (<em>NN</em>). 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 (<em>TSV</em>). However, the <em>SVM</em> algorithm improves 50 % accuracy by considering <em>SP</em> input for thermal comfort (<em>TC</em>). Spatial parameters, including the distance between windows and air conditioning units, emerge as critical factors influencing thermal comfort.</div></div>\",\"PeriodicalId\":9273,\"journal\":{\"name\":\"Building and Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Building and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0360132324009259\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Building and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360132324009259","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
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 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.
期刊介绍:
Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.