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Title: A review of machine learning in building load prediction

Journal Article · · Applied Energy
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [3];  [4];  [5]; ORCiD logo [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Drexel Univ., Philadelphia, PA (United States)
  3. Univ. of Utah, Salt Lake City, UT (United States)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  5. Texas A & M Univ., College Station, TX (United States)
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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.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1772454
Alternate ID(s):
OSTI ID: 1778629
Report Number(s):
NREL/JA-5500-76754; MainId:9415; UUID:256b4af9-8739-453d-a1c6-063aec3b72d6; MainAdminID:20099
Journal Information:
Applied Energy, Vol. 285; ISSN 0306-2619
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
building energy system
load prediction
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