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Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization

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Listed:
  • Mengran Zhou

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Tianyu Hu

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Kai Bian

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Wenhao Lai

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Feng Hu

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Oumaima Hamrani

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Ziwei Zhu

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

Abstract

Short-term electric load forecasting plays a significant role in the safe and stable operation of the power system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R 2 is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.

Suggested Citation

  • Mengran Zhou & Tianyu Hu & Kai Bian & Wenhao Lai & Feng Hu & Oumaima Hamrani & Ziwei Zhu, 2021. "Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization," Energies, MDPI, vol. 14(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4890-:d:611990
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    References listed on IDEAS

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    2. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
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    6. Andriy Chaban & Marek Lis & Andrzej Szafraniec & Vitaliy Levoniuk, 2022. "An Application of the Hamilton–Ostrogradsky Principle to the Modeling of an Asymmetrically Loaded Three-Phase Power Line," Energies, MDPI, vol. 15(21), pages 1-19, November.
    7. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Dimitrios Kontogiannis & Athanasios Fevgas & Miltiadis Alamaniotis, 2022. "Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques," Energies, MDPI, vol. 15(21), pages 1-24, October.

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