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Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

Author

Listed:
  • Bingjie Jin

    (Power Grid Planning Center of Guangdong Power Grid Company, Guangzhou 510080, China)

  • Guihua Zeng

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Zhilin Lu

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Hongqiao Peng

    (Power Grid Planning Center of Guangdong Power Grid Company, Guangzhou 510080, China)

  • Shuxin Luo

    (Power Grid Planning Center of Guangdong Power Grid Company, Guangzhou 510080, China)

  • Xinhe Yang

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Haojun Zhu

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510663, China)

  • Mingbo Liu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.

Suggested Citation

  • Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7584-:d:942138
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    References listed on IDEAS

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    1. Waqas Ahmad & Nasir Ayub & Tariq Ali & Muhammad Irfan & Muhammad Awais & Muhammad Shiraz & Adam Glowacz, 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine," Energies, MDPI, vol. 13(11), pages 1-17, June.
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    Cited by:

    1. Bibi Ibrahim & Luis Rabelo & Alfonso T. Sarmiento & Edgar Gutierrez-Franco, 2023. "A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics," Energies, MDPI, vol. 16(13), pages 1-29, July.

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