CVAE-Transformer-based industrial short-term load forecasting

Accurate industrial load forecasting is essential for maintaining a balance between power supply and demand within intelligent power systems. In response to the challenges presented by the intricate, nonlinear, and temporal nature of industrial load data, a novel method integrating a Conditional Var...

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Published in:	Journal of physics. Conference series Vol. 3043; no. 1; pp. 12149 - 12154
Main Authors:	Li, Jiawei, Wang, Yuanyuan, Liu, Yonghuan, Liao, Xiaoyu
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01.06.2025
Subjects:	Electrical loads Feature extraction Forecasting
ISSN:	1742-6588, 1742-6596
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Abstract	Accurate industrial load forecasting is essential for maintaining a balance between power supply and demand within intelligent power systems. In response to the challenges presented by the intricate, nonlinear, and temporal nature of industrial load data, a novel method integrating a Conditional Variational Autoencoder (CVAE) with a Transformer is introduced. The CVAE, conditioned on meteorological and economic data, excels in precise feature extraction from load data, effectively identifying critical patterns that influence load behavior. This specialized feature extraction is complemented by the Transformer, which refines the understanding of complex load dynamics through its temporal encoding and attention mechanisms. Experimental results using datasets from China and South Korea reveal substantial enhancements in forecasting precision compared to current models
AbstractList	Accurate industrial load forecasting is essential for maintaining a balance between power supply and demand within intelligent power systems. In response to the challenges presented by the intricate, nonlinear, and temporal nature of industrial load data, a novel method integrating a Conditional Variational Autoencoder (CVAE) with a Transformer is introduced. The CVAE, conditioned on meteorological and economic data, excels in precise feature extraction from load data, effectively identifying critical patterns that influence load behavior. This specialized feature extraction is complemented by the Transformer, which refines the understanding of complex load dynamics through its temporal encoding and attention mechanisms. Experimental results using datasets from China and South Korea reveal substantial enhancements in forecasting precision compared to current models
Author	Wang, Yuanyuan Li, Jiawei Liu, Yonghuan Liao, Xiaoyu
Author_xml	– sequence: 1 givenname: Jiawei surname: Li fullname: Li, Jiawei organization: Changsha University of Science & Technology State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha 410114, China – sequence: 2 givenname: Yuanyuan surname: Wang fullname: Wang, Yuanyuan organization: Changsha University of Science & Technology State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha 410114, China – sequence: 3 givenname: Yonghuan surname: Liu fullname: Liu, Yonghuan organization: Changsha University of Science & Technology State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha 410114, China – sequence: 4 givenname: Xiaoyu surname: Liao fullname: Liao, Xiaoyu organization: Changsha University of Science & Technology State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha 410114, China
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Cites_doi	10.1109/TCE.2023.3266506 10.1109/TPWRD.2022.3178822 10.1016/j.conbuildmat.2020.120198 10.1109/IEEM58616.2023.10406604 10.1016/j.seta.2022.102209 10.1016/j.ijforecast.2021.03.012 10.1016/j.egyr.2023.01.060
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References	Pereira (JPCS_3043_1_012149bib9) 2022 Chafak (JPCS_3043_1_012149bib3) 2023; 9 Yi (JPCS_3043_1_012149bib11) 2023; 69 Mosbeh (JPCS_3043_1_012149bib10) 2020; 264 Wen (JPCS_3043_1_012149bib1) 2022 Baur (JPCS_3043_1_012149bib7) 2023 Bryan (JPCS_3043_1_012149bib6) 2021; 37 Yuanyuan (JPCS_3043_1_012149bib2) 2021; 129 Arash (JPCS_3043_1_012149bib12) 2022; 52 Xin (JPCS_3043_1_012149bib4) 2023; 38 Shiyi (JPCS_3043_1_012149bib8) 2022; 71 Khazeiynasab (JPCS_3043_1_012149bib13) 2023 Yuanyuan (JPCS_3043_1_012149bib5) 2020; 36
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Title	CVAE-Transformer-based industrial short-term load forecasting
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