Electrical load prediction of healthcare buildings through single and ensemble learning

• Published in: Energy reports Vol. 6; pp. 2751 - 2767
• Main Authors: Cao, Lingyan, Li, Yongkui, Zhang, Jiansong, Jiang, Yi, Han, Yilong, Wei, Jianjun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-11-2020; The Authors. Published by Elsevier Ltd; Elsevier
• Subjects: Ensemble model machine learning; Healthcare buildings; Load prediction; Random forest; XGBoost; Random forest; Healthcare buildings; Load prediction; Ensemble model machine learning; XGBoost
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2020.10.005

Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of hospital energy consumption is of paramount importance to a successful healthcare building energy management. In this study, eight machine learning models of single learning and ensemble learning were developed for predicting healthcare facilities’ energy consumption. To validate the performance of the proposed model, an experiment was conducted on a general hospital in Shanghai, China. It was found that the two ensemble models, Extreme Gradient Boosting (XGBoost) model and Random Forest (RF) model, outperformed single models in daily electrical load prediction. A further comparison between models trained with daily and weekly temporal resolution electrical data shows that it is more likely to achieve higher accuracy with finer time granularity. Through feature importance analysis, the most influential features under the daily and weekly electrical load prediction were identified. Based on the prediction results, it is expected that hospital facility managers will be able to conveniently assess the expected energy usage of their hospitals with the machine learning models.