A hybrid agent-based machine learning method for human-centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption. Due to a variety of reasons (e.g., underperforming building energy management systems or restrictions due to privacy policies), the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption. Therefore, this study proposed an agent-based machine learning model whereby agent-based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction. Boruta feature selection was also introduced in this study to select all relevant features. The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data, with even greater improvements after conducting Boruta feature selection.