Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest
Establishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not we...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/14/2539 |
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| _version_ | 1850077742244036608 |
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| author | Hao Yang Maoyu Ran |
| author_facet | Hao Yang Maoyu Ran |
| author_sort | Hao Yang |
| collection | DOAJ |
| description | Establishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not well suited for practical applications. In this study, real thermal sensation survey data were collected and used to first analyze the discrepancy between PMV model predictions and actual human thermal sensation. Subsequently, a simple thermal sensation prediction model was developed using multiple linear regression. More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. Finally, the best-performing model was further optimized using Bayesian methods to enhance hyperparameter tuning efficiency and improve the accuracy of personalized human thermal sensation prediction. |
| format | Article |
| id | doaj-art-01adee6085ce40f7ab4b6477bddb9b37 |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-01adee6085ce40f7ab4b6477bddb9b372025-08-20T02:45:45ZengMDPI AGBuildings2075-53092025-07-011514253910.3390/buildings15142539Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random ForestHao Yang0Maoyu Ran1School of Architecture, Huaqiao University, Xiamen 361021, ChinaSchool of Architecture, Huaqiao University, Xiamen 361021, ChinaEstablishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not well suited for practical applications. In this study, real thermal sensation survey data were collected and used to first analyze the discrepancy between PMV model predictions and actual human thermal sensation. Subsequently, a simple thermal sensation prediction model was developed using multiple linear regression. More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. Finally, the best-performing model was further optimized using Bayesian methods to enhance hyperparameter tuning efficiency and improve the accuracy of personalized human thermal sensation prediction.https://www.mdpi.com/2075-5309/15/14/2539Bayesian optimizationRandom Forest (RF)human thermal comfortmachine learningPMV |
| spellingShingle | Hao Yang Maoyu Ran Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest Buildings Bayesian optimization Random Forest (RF) human thermal comfort machine learning PMV |
| title | Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest |
| title_full | Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest |
| title_fullStr | Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest |
| title_full_unstemmed | Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest |
| title_short | Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest |
| title_sort | personalized human thermal sensation prediction based on bayesian optimized random forest |
| topic | Bayesian optimization Random Forest (RF) human thermal comfort machine learning PMV |
| url | https://www.mdpi.com/2075-5309/15/14/2539 |
| work_keys_str_mv | AT haoyang personalizedhumanthermalsensationpredictionbasedonbayesianoptimizedrandomforest AT maoyuran personalizedhumanthermalsensationpredictionbasedonbayesianoptimizedrandomforest |