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A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings
Highlights The prediction of occupancy and window-opening behaviour using ML models was reviewed. Temperature is the most important predictor variable for window-opening behaviour. Logistic regression does not perform well for the “cut-off” temperature. Integrating occupancy into HVAC system control can reduce energy consumption by 23%.
Abstract This study carries out a literature review on studies using machine learning (ML) models to predict occupancy and window-opening behaviour and their application in smart buildings. For occupant number/level prediction, the indoor concentration is an important predictor variable. To further improve the model performance, it is recommended to use feature engineering to create additional -related predictor variables. For the window-opening behaviour, the outdoor temperature, indoor temperature and wind speed are the top three most important predictor variables. The window-opening probability usually increases as the outdoor temperature increases. It is not recommended to use both indoor and outdoor temperatures as predictor variables when buildings are free-running. In regard to model selection, although logistic regression is the most common model for the window-opening behaviour, it does not perform well for the “cut-off” temperature. From another aspect, it can be inferred that although an artificial neural network may have better prediction accuracy, its transferability might not be as good as that of traditional ML models. It is recommended to compare the performances of different ML models using a few criteria to find the most suitable model. In the future, to further improve the prediction accuracy, the model can take building characteristics and occupant features into consideration. In regard to the application of ML models, the energy consumption of heating, ventilation and air-conditioning (HVAC) systems can be reduced by 23% on average by optimizing the control strategy of HVAC systems according to the occupancy information predicted by the ML models.
A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings
Highlights The prediction of occupancy and window-opening behaviour using ML models was reviewed. Temperature is the most important predictor variable for window-opening behaviour. Logistic regression does not perform well for the “cut-off” temperature. Integrating occupancy into HVAC system control can reduce energy consumption by 23%.
Abstract This study carries out a literature review on studies using machine learning (ML) models to predict occupancy and window-opening behaviour and their application in smart buildings. For occupant number/level prediction, the indoor concentration is an important predictor variable. To further improve the model performance, it is recommended to use feature engineering to create additional -related predictor variables. For the window-opening behaviour, the outdoor temperature, indoor temperature and wind speed are the top three most important predictor variables. The window-opening probability usually increases as the outdoor temperature increases. It is not recommended to use both indoor and outdoor temperatures as predictor variables when buildings are free-running. In regard to model selection, although logistic regression is the most common model for the window-opening behaviour, it does not perform well for the “cut-off” temperature. From another aspect, it can be inferred that although an artificial neural network may have better prediction accuracy, its transferability might not be as good as that of traditional ML models. It is recommended to compare the performances of different ML models using a few criteria to find the most suitable model. In the future, to further improve the prediction accuracy, the model can take building characteristics and occupant features into consideration. In regard to the application of ML models, the energy consumption of heating, ventilation and air-conditioning (HVAC) systems can be reduced by 23% on average by optimizing the control strategy of HVAC systems according to the occupancy information predicted by the ML models.
A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings
Dai, Xilei (author) / Liu, Junjie (author) / Zhang, Xin (author)
Energy and Buildings ; 223
2020-05-17
Article (Journal)
Electronic Resource
English
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