Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection
Occupant behavior based on natural ventilation has a significant impact on building energy consumption. It is important for the quantification of occupant-behavior models to select observed variables, i.e., features that affect the state of window opening and closing, and to consider machine learnin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/16/5993 |
| _version_ | 1797445760300613632 |
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| author | Kaito Furuhashi Takashi Nakaya Yoshihiro Maeda |
| author_facet | Kaito Furuhashi Takashi Nakaya Yoshihiro Maeda |
| author_sort | Kaito Furuhashi |
| collection | DOAJ |
| description | Occupant behavior based on natural ventilation has a significant impact on building energy consumption. It is important for the quantification of occupant-behavior models to select observed variables, i.e., features that affect the state of window opening and closing, and to consider machine learning models that are effective in predicting this state. In this study, thermal comfort was investigated, and machine learning data were analyzed for 30 houses in Gifu, Japan. Among the selected machine learning models, the logistic regression and deep neural network models produced consistently excellent results. The accuracy of the prediction of open and closed windows differed among the models, and the factors influencing the window-opening behaviors of the occupants differed from those influencing their window-closing behavior. In the selection of features, the analysis using thermal indices representative of the room and cooling features showed excellent results, indicating that cooling features, which have conflicting relationships with natural ventilation, are useful for improving the accuracy of occupant-behavior prediction. The present study indicates that building designers should incorporate occupant behavior based on natural ventilation into their designs. |
| first_indexed | 2024-03-09T13:30:28Z |
| format | Article |
| id | doaj.art-b797dc5181bd4e15ad64a851d2e7b726 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T13:30:28Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b797dc5181bd4e15ad64a851d2e7b7262023-11-30T21:19:02ZengMDPI AGEnergies1996-10732022-08-011516599310.3390/en15165993Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature SelectionKaito Furuhashi0Takashi Nakaya1Yoshihiro Maeda2Faculty of Engineering, Department of Architecture, Shinshu University, Nagano 380-0928, JapanFaculty of Engineering, Department of Architecture, Shinshu University, Nagano 380-0928, JapanFaculty of Engineering, Department of Electrical Engineering, Tokyo University of Science (TUS), Tokyo 125-8585, JapanOccupant behavior based on natural ventilation has a significant impact on building energy consumption. It is important for the quantification of occupant-behavior models to select observed variables, i.e., features that affect the state of window opening and closing, and to consider machine learning models that are effective in predicting this state. In this study, thermal comfort was investigated, and machine learning data were analyzed for 30 houses in Gifu, Japan. Among the selected machine learning models, the logistic regression and deep neural network models produced consistently excellent results. The accuracy of the prediction of open and closed windows differed among the models, and the factors influencing the window-opening behaviors of the occupants differed from those influencing their window-closing behavior. In the selection of features, the analysis using thermal indices representative of the room and cooling features showed excellent results, indicating that cooling features, which have conflicting relationships with natural ventilation, are useful for improving the accuracy of occupant-behavior prediction. The present study indicates that building designers should incorporate occupant behavior based on natural ventilation into their designs.https://www.mdpi.com/1996-1073/15/16/5993occupant behaviornatural ventilationmachine learningpredictionJapanese dwellings |
| spellingShingle | Kaito Furuhashi Takashi Nakaya Yoshihiro Maeda Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection Energies occupant behavior natural ventilation machine learning prediction Japanese dwellings |
| title | Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection |
| title_full | Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection |
| title_fullStr | Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection |
| title_full_unstemmed | Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection |
| title_short | Prediction of Occupant Behavior toward Natural Ventilation in Japanese Dwellings: Machine Learning Models and Feature Selection |
| title_sort | prediction of occupant behavior toward natural ventilation in japanese dwellings machine learning models and feature selection |
| topic | occupant behavior natural ventilation machine learning prediction Japanese dwellings |
| url | https://www.mdpi.com/1996-1073/15/16/5993 |
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