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author:

Liao, Honglin (Liao, Honglin.) [1] | Wu, Zheng (Wu, Zheng.) [2] | Ye, Hanhao (Ye, Hanhao.) [3] | Hu, Pengxiang (Hu, Pengxiang.) [4] | Du, Wei (Du, Wei.) [5] | Tang, Yong (Tang, Yong.) [6]

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EI Scopus

Abstract:

Power system transformation is crucial in a dual-carbon context. In this paper, we propose an optimized Transformer architecture, Romember model, which improves the accuracy of power consumption prediction by introducing rotational position coding. We construct a multidimensional evaluation framework to provide guidelines for the continuous optimization of the model. The Romember model contributes to balanced grid dispatch, optimal load allocation, and the promotion of energy green transition. © 2024 IEEE.

Keyword:

Deep learning Electric load dispatching Energy management Forecasting Optimization

Community:

  • [ 1 ] [Liao, Honglin]Fuzhou University, Fuzhou, China
  • [ 2 ] [Wu, Zheng]Fuzhou University, Fuzhou, China
  • [ 3 ] [Ye, Hanhao]Fuzhou University, Fuzhou, China
  • [ 4 ] [Hu, Pengxiang]Fuzhou University, Fuzhou, China
  • [ 5 ] [Du, Wei]Fuzhou University, Fuzhou, China
  • [ 6 ] [Tang, Yong]Shandong University, Jinan, China

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Year: 2024

Page: 946-950

Language: English

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ESI Highly Cited Papers on the List: 0 Unfold All

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Chinese Cited Count:

30 Days PV: 2

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