Field data in terms of both occupant window behavior and relevant influential factors were collected from real residential buildings during transitional seasons. In this study, this algorithm has been adopted to model and predict occupant window behavior, aiming to further improve the modeling accuracy from a globally accepted modeling approach, namely, Logistic Regression Analysis.

XGBoost is a recently introduced machine learning algorithm, which has been proven as very powerful in modeling complicated processes in other research fields. During this work, many mathematical and machine learning methods have been used, and some level of prediction accuracy has been achieved. Due to the lack of good behavioral models in current simulation packages, many researchers have tried to develop useful behavioral models to improve simulation accuracy, including window behavior models, using field data collected from real buildings. Buildings account for over 32% of total society energy consumption, and to make buildings more energy efficient dynamic building performance simulation has been widely adopted during the buildings’ design to help select most appropriate HVAC (Heating Ventilation and Air Conditioning) systems.

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