Ensemble learning for electricity consumption forecasting in office buildings

NEUROCOMPUTING

卷:423

Article

Pinto, Tiago¹  Praca, Isabel¹  Vale, Zita²  Silva, Jose² 

[1] Inst Super Engn Porto, GECAD Res Grp, R Dr Antonio Bernardino Almeida 431, P-4249015 Porto, Portugal

[2] Inst Politecn Porto, Inst Super Engn Porto, R Dr Antonio Bernardino Almeida 431, P-4249015 Porto, Portugal

关键词: Energy consumption;    Ensemble learning;    Machine learning;    Load forecasting;   

DOI  :  10.1016/j.neucom.2020.02.124

来源: Elsevier

【 摘 要 】

This paper presents three ensemble learning models for short term load forecasting. Machine learning has evolved quickly in recent years, leading to novel and advanced models that are improving the forecasting results in multiple fields. However, in highly dynamic fields such as power and energy systems, dealing with the fast acquisition of large amounts of data from multiple data sources and taking advantage from the correlation between the multiple available variables is a challenging task, for which current models are not prepared. Ensemble learning is bringing promising results in this sense, as, by combining the results and use of multiple learners, is able to find new ways for current learning models to be used and optimized. In this paper three ensemble learning models are developed and the respective results compared: gradient boosted regression trees, random forests and an adaptation of Adaboost. Results for electricity consumption forecasting in hour-ahead are presented using a case-study based on real data from an office building. Results show that the adapted Adaboost model outperforms the reference models for hour-ahead load forecasting. (C) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

Free