查看文章

Machine learning (ML) methods have been widely applied in predicting energy consumption of buildings. As data-intensive methods, the performance of prediction to a great extent depends on the quality of data. Lacking input features of data will render underfitting problems that significantly impede prediction performance. Currently, a considerable number of buildings are suffering from data availability issues, due to underperforming building energy management systems. A comprehensive understanding of the implications of accurately predicting the energy consumption of buildings using ML methods with limited data is essential for building energy efficiency and energy planning. However, the research in this area is still at the preliminary stage.