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Short Term Electrical Load Combination Forecasting Model based on Multi-dimensional Meteorological Information Spatio-Temporal Fusion and MPA-VMD
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  • Ling Yun Wang,
  • Xiang Zhou,
  • Honglei Xu ,
  • Tian Tian,
  • Huamin Tong
Ling Yun Wang
China Three Gorges University
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Xiang Zhou
China Three Gorges University

Corresponding Author:[email protected]

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Honglei Xu
Curtin University School of Electrical Engineering Computing and Mathematical Sciences
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Tian Tian
China Three Gorges University
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Huamin Tong
State Grid Hubei Electric Power Co Yichang Power Supply Company
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This paper considers the variability in the impact of multi-dimensional meteorological information on power load in different regions. To improve the accuracy of load forecasting in the spatial dimension, the method of spatio-temporal fusion (SF) of multi-dimensional meteorological information is proposed. The Copula theory is applied to analyze the nonlinear coupling of meteorological information such as wind speed, rainfall, temperature, and sunshine intensity from multiple meteorological stations with the power load and to achieve spatio-temporal fusion. In the time dimension, the core parameters of the variational mode decomposition (VMD) are improved by the marine predators algorithm (MPA). The weighted permutation entropy (WPE) is used to construct the MPA-VMD fitness function for the adaptive decomposition of the load sequence. In addition, the input sets of the LSTM model and MPA-LSSVM model are constructed by combining each component of the time dimension and each meteorological information of spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index, and reconstructed to obtain the overall prediction results. The analysis results show that the proposed forecasting method is better than the traditional forecasting method and effectively improves the accuracy of power load forecasting.
09 Mar 2023Submitted to IET Generation, Transmission & Distribution
09 Mar 2023Submission Checks Completed
09 Mar 2023Assigned to Editor
10 Mar 2023Reviewer(s) Assigned
05 May 2023Review(s) Completed, Editorial Evaluation Pending
08 May 2023Editorial Decision: Revise Major
21 Jun 20231st Revision Received
23 Jun 2023Submission Checks Completed
23 Jun 2023Assigned to Editor
24 Jun 2023Reviewer(s) Assigned
11 Jul 2023Review(s) Completed, Editorial Evaluation Pending
22 Jul 2023Editorial Decision: Revise Major
06 Aug 20232nd Revision Received
09 Aug 2023Submission Checks Completed
09 Aug 2023Assigned to Editor
17 Aug 2023Reviewer(s) Assigned
08 Sep 2023Review(s) Completed, Editorial Evaluation Pending
12 Sep 2023Editorial Decision: Accept