Energy management in modern buildings based on demand prediction and machine learning—A review

dc.contributor.author	Moghimi, Seyed Morteza
dc.contributor.author	Gulliver, Thomas Aaron
dc.contributor.author	Thirumai Chelvan, Ilamparithi
dc.date.accessioned	2024-10-10T17:23:13Z
dc.date.available	2024-10-10T17:23:13Z
dc.date.issued	2024
dc.description.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.
dc.description.reviewstatus	Reviewed
dc.description.scholarlevel	Faculty
dc.identifier.citation	Moghimi, S. M., Gulliver, T. A., & Thirumai Chelvan, I. (2024). Energy management in modern buildings based on demand prediction and machine learning—A review. Energies, 17(3), Article 3. https://doi.org/10.3390/en17030555
dc.identifier.uri	https://doi.org/10.3390/en17030555
dc.identifier.uri	https://hdl.handle.net/1828/20566
dc.language.iso	en
dc.publisher	Energies
dc.rights	Attribution CC BY
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	demand response
dc.subject	energy flexibility
dc.subject	green buildings
dc.subject	machine learning
dc.subject	optimization
dc.subject	smart buildings
dc.title	Energy management in modern buildings based on demand prediction and machine learning—A review
dc.type	Article

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