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Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm

Author

Listed:
  • Suqi Zhang

    (School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China)

  • Ningjing Zhang

    (School of Science, Tianjin University of Commerce, Tianjin 300134, China)

  • Ziqi Zhang

    (China Construction Second Engineering Bureau Ltd., South China Company, Shenzhen 518000, China)

  • Ying Chen

    (State Grid Tianjin Marketing Service Center (Metrology Center), Tianjin 300200, China)

Abstract

Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved Seagull Optimization Algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved Seagull Optimization Algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the Seagull Optimization Algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.

Suggested Citation

  • Suqi Zhang & Ningjing Zhang & Ziqi Zhang & Ying Chen, 2022. "Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm," Energies, MDPI, vol. 15(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9197-:d:993217
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    Cited by:

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    2. Laiqing Yan & Zutai Yan & Zhenwen Li & Ning Ma & Ran Li & Jian Qin, 2023. "Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm," Energies, MDPI, vol. 16(13), pages 1-18, July.
    3. Cuiping Zhou & Shaobo Li & Cankun Xie & Panliang Yuan & Xiangfu Long, 2024. "MISAO: A Multi-Strategy Improved Snow Ablation Optimizer for Unmanned Aerial Vehicle Path Planning," Mathematics, MDPI, vol. 12(18), pages 1-37, September.
    4. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos & Constantinos Hilas, 2023. "Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network," Energies, MDPI, vol. 16(10), pages 1-20, May.
    5. Leonardo Brain García Fernández & Anna Diva Plasencia Lotufo & Carlos Roberto Minussi, 2023. "Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy," Energies, MDPI, vol. 16(10), pages 1-30, May.
    6. Shakeel, Asim & Chong, Daotong & Wang, Jinshi, 2023. "Load forecasting of district heating system based on improved FB-Prophet model," Energy, Elsevier, vol. 278(C).

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