A Review of Machine Learning in Building Load Prediction

Liang Zhang, Jin Wen, Yanfei Li, Jianli Chen, Yunyang Ye, Yangyang Fu, William Livingood

Research output: Contribution to journalArticlepeer-review

279 Scopus Citations

Abstract

The surge of machine learning and increasing data accessibility in buildings provide great opportunities for applying machine learning to building energy system modeling and analysis. Building load prediction is one of the most critical components for many building control and analytics activities, as well as grid-interactive and energy efficiency building operation. While a large number of research papers exist on the topic of machine-learning-based building load prediction, a comprehensive review from the perspective of machine learning is missing. In this paper, we review the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E. Firstly, we review the applications of building load prediction model (task T). Then, we review the modeling algorithms that improve machine learning performance and accuracy (performance P). Throughout the papers, we also review the literature from the data perspective for modeling (experience E), including data engineering from the sensor level to data level, pre-processing, feature extraction and selection. Finally, we conclude with a discussion of well-studied and relatively unexplored fields for future research reference. We also identify the gaps in current machine learning application and predict for future trends and development.

Original languageAmerican English
Article number116452
Number of pages22
JournalApplied Energy
Volume285
DOIs
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5500-76754

Keywords

  • Building energy forecasting
  • Building energy system
  • Building load prediction
  • Data engineering
  • Feature engineering
  • Machine learning

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