The application of machine learning(ML)modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment.Although many advancements have been made,no standardized ML m...The application of machine learning(ML)modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment.Although many advancements have been made,no standardized ML modelling framework exists in daylight assessment.In this study,625 different building layouts were generated to model useful daylight illuminance(UDI).Two state-of-the-art ML algorithms,eXtreme Gradient Boosting(XGBoost)and random forest(RF),were employed to analyze UDI in four categories:UDI-f(fell short),UDI-s(supplementary),UDI-a(autonomous),and UDI-e(exceeded).A feature(internal finish)was introduced to the framework to better reflect real-world representation.The results show that XGBoost models predict UDI with a maximum accuracy of R^(2)=0.992.Compared to RF,the XGBoost ML models can significantly reduce prediction errors.Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.展开更多
基金The authors are grateful for support from the Australian Research Council(ARC)through the Linkage Infrastructure,Equipment and Facilities(LE210100019).The assistance of the ASCII Lab members at Monash University is greatly appreciated.
文摘The application of machine learning(ML)modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment.Although many advancements have been made,no standardized ML modelling framework exists in daylight assessment.In this study,625 different building layouts were generated to model useful daylight illuminance(UDI).Two state-of-the-art ML algorithms,eXtreme Gradient Boosting(XGBoost)and random forest(RF),were employed to analyze UDI in four categories:UDI-f(fell short),UDI-s(supplementary),UDI-a(autonomous),and UDI-e(exceeded).A feature(internal finish)was introduced to the framework to better reflect real-world representation.The results show that XGBoost models predict UDI with a maximum accuracy of R^(2)=0.992.Compared to RF,the XGBoost ML models can significantly reduce prediction errors.Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.
