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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumai Chelvan, 2024. "Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review," Energies, MDPI, vol. 17(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:555-:d:1324854
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    References listed on IDEAS

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    Cited by:

    1. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumarai Chelvan & Hossen Teimoorinia, 2024. "Resource Optimization for Grid-Connected Smart Green Townhouses Using Deep Hybrid Machine Learning," Energies, MDPI, vol. 17(23), pages 1-31, December.
    2. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumarai Chelvan & Hossen Teimoorinia, 2024. "Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode," Energies, MDPI, vol. 17(24), pages 1-25, December.

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