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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| Vydáno v: | Journal of physics. Conference series Ročník 3043; číslo 1; s. 12149 - 12154 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Bristol
IOP Publishing
01.06.2025
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| Témata: | |
| ISSN: | 1742-6588, 1742-6596 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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 |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1742-6588 1742-6596 |
| DOI: | 10.1088/1742-6596/3043/1/012149 |