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High Precision LSTM Model for Short-Time Load Forecasting in Power Systems

Author

Listed:
  • Tomasz Ciechulski

    (Institute of Electronic Systems, Faculty of Electronics, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland)

  • Stanisław Osowski

    (Institute of Electronic Systems, Faculty of Electronics, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
    Faculty of Electrical Engineering, Warsaw University of Technology, pl. Politechniki 1, 00-661 Warsaw, Poland)

Abstract

The paper presents the application of recurrent LSTM neural networks for short-time load forecasting in the Polish Power System (PPS) and a small region of a power system in Central Poland. The objective of the present work was to develop an efficient and accurate method of forecasting the 24-h pattern of power load with a 1-h and 24-h horizon. LSTM showed effectiveness in predicting the irregular trends in time series. The final forecast is estimated using an ensemble consisted of five independent predictions. Numerical experiments proved the superiority of the ensemble above single predictor resulting in a reduction of the MAPE the RMSE error by more than 6% in both forecasting tasks.

Suggested Citation

  • Tomasz Ciechulski & Stanisław Osowski, 2021. "High Precision LSTM Model for Short-Time Load Forecasting in Power Systems," Energies, MDPI, vol. 14(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:2983-:d:559287
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    References listed on IDEAS

    as
    1. Tomasz Ciechulski & Stanisław Osowski, 2020. "Deep Learning Approach to Power Demand Forecasting in Polish Power System," Energies, MDPI, vol. 13(22), pages 1-13, November.
    2. Alfredo Nespoli & Emanuele Ogliari & Silvia Pretto & Michele Gavazzeni & Sonia Vigani & Franco Paccanelli, 2021. "Electrical Load Forecast by Means of LSTM: The Impact of Data Quality," Forecasting, MDPI, vol. 3(1), pages 1-11, February.
    3. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
    4. Ibrahim Salem Jahan & Vaclav Snasel & Stanislav Misak, 2020. "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, MDPI, vol. 13(22), pages 1-12, November.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Robert Basmadjian & Amirhossein Shaafieyoun & Sahib Julka, 2021. "Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods," Energies, MDPI, vol. 14(21), pages 1-23, November.
    3. Stanislaw Osowski & Robert Szmurlo & Krzysztof Siwek & Tomasz Ciechulski, 2022. "Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study," Energies, MDPI, vol. 15(9), pages 1-21, April.
    4. Zhoufan Chen & Congmin Wang & Longjin Lv & Liangzhong Fan & Shiting Wen & Zhengtao Xiang, 2023. "Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
    5. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
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    7. Wenna Zhao & Guoxing Mu & Yanfang Zhu & Limei Xu & Deliang Zhang & Hongwei Huang, 2023. "Research on Electric Load Forecasting and User Benefit Maximization Under Demand-Side Response," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-20, January.
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    9. Bibi Ibrahim & Luis Rabelo & Edgar Gutierrez-Franco & Nicolas Clavijo-Buritica, 2022. "Machine Learning for Short-Term Load Forecasting in Smart Grids," Energies, MDPI, vol. 15(21), pages 1-19, October.

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