Electrical load prediction of healthcare buildings through single and ensemble learning

Energy Reports

Yilong Han¹  Yongkui Li²  Jiansong Zhang³  Lingyan Cao³  Jianjun Wei⁴  Yi Jiang⁴ 

[1]Corresponding author.

[2]School of Construction Management Technology, Purdue University, 401 N Grant St., West Lafayette, IN 47907, USA

[3]Department of Construction Management and Real Estate, School of Economics and Management, Tongji University, 1500 Siping Road, Shanghai, 200092, China

[4]School of Construction Management Technology, Purdue University, 401 N Grant St., West Lafayette, IN 47907, USA

关键词: Healthcare buildings;    Load prediction;    Ensemble model machine learning;    XGBoost;    Random forest;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown