Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review

Seyed Morteza Moghimi (), Thomas Aaron Gulliver and Ilamparithi Thirumai Chelvan
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Seyed Morteza Moghimi: Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada
Thomas Aaron Gulliver: Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada
Ilamparithi Thirumai Chelvan: Department of Electrical and Computer Engineering, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada

Energies, 2024, vol. 17, issue 3, 1-20

Abstract: Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP.

Keywords: demand response; energy flexibility; green buildings; machine learning; optimization; smart buildings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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