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Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost Model

Author

Listed:
  • Hao Ma

    (State Grid Hebei Marketing Service Center, Shijiazhuang 050021, China)

  • Peng Yang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
    State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Fei Wang

    (Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

  • Xiaotian Wang

    (State Grid Hebei Marketing Service Center, Shijiazhuang 050021, China)

  • Di Yang

    (State Grid Hebei Marketing Service Center, Shijiazhuang 050021, China)

  • Bo Feng

    (State Grid Hebei Marketing Service Center, Shijiazhuang 050021, China)

Abstract

In order to effectively carry out the heavy overload monitoring and maintenance of public transformers in the distribution network, ensure the reliability of the distribution network power supply, and improve customer satisfaction with electricity consumption, this paper presents a short-term heavy overload forecasting method for public transformers based on the LSTM-XGBOOST combined model. The model extracts heavy overload feature variables from four dimensions, including basic parameter information, weather, time, and recent load, and constructs a short-term second highest load prediction model based on the LSTM algorithm to obtain the predicted value of the second highest load rate. After aggregating the heavy overload feature variables and the predicted second highest load rate, the XGboost algorithm is employed to construct a short-term heavy overload prediction model for public transformers to judge whether the public transformers display heavy overload. The test results show that this method has high accuracy in short-term heavy overload forecasting, and can effectively assist in the key monitoring and control of heavy overload in public transformers.

Suggested Citation

  • Hao Ma & Peng Yang & Fei Wang & Xiaotian Wang & Di Yang & Bo Feng, 2023. "Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost Model," Energies, MDPI, vol. 16(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1507-:d:1056559
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    References listed on IDEAS

    as
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    4. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    5. Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.
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