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Very Short-Term Load Forecasting for Large Power Systems with Kalman Filter-Based Pseudo-Trend Information Using LSTM

Author

Listed:
  • Tae-Geun Kim

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea)

  • Bo-Sung Kwon

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea)

  • Sung-Guk Yoon

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea
    Department of Convergence of Energy Policy and Technology, Soongsil University, Seoul 06978, Republic of Korea)

  • Kyung-Bin Song

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea
    Department of Convergence of Energy Policy and Technology, Soongsil University, Seoul 06978, Republic of Korea)

Abstract

The increasing integration of renewable energy resources, driven by carbon neutrality goals, has intensified load variability, thereby making very short-term load forecasting (VSTLF) more challenging. Accurate VSTLF is essential for the reliable and economical real-time operation of power systems. This study proposes a Long Short-Term Memory (LSTM)-based VSTLF model designed to predict nationwide power system load, including renewable generation over a six-hour horizon with 15 min intervals. The model employs a reconstituted load approach that incorporates photovoltaic (PV) generation effects and computes representative weather variables across the country. Furthermore, the most informative input features are selected through a combination of correlation analyses. To further enhance input sequences, pseudo-trend components are generated using a Kalman filter-based predictor and integrated into the model input. The Kalman filter-based pseudo-trend produced an MAPE of 1.724%, and its inclusion in the proposed model reduced the forecasting error (MAPE) by 0.834 percentage points. Consequently, the final model achieved an MAPE of 0.890%, which is under 1% of the 94,929 MW nationwide peak load.

Suggested Citation

  • Tae-Geun Kim & Bo-Sung Kwon & Sung-Guk Yoon & Kyung-Bin Song, 2025. "Very Short-Term Load Forecasting for Large Power Systems with Kalman Filter-Based Pseudo-Trend Information Using LSTM," Energies, MDPI, vol. 18(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4890-:d:1749498
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    References listed on IDEAS

    as
    1. Ziel, Florian & Liu, Bidong, 2016. "Lasso estimation for GEFCom2014 probabilistic electric load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1029-1037.
    2. Wang, Chuang & Zhao, Haishen & Liu, Yang & Fan, Guojin, 2024. "Minute-level ultra-short-term power load forecasting based on time series data features," Applied Energy, Elsevier, vol. 372(C).
    3. Tae-Geun Kim & Sung-Guk Yoon & Kyung-Bin Song, 2025. "Very Short-Term Load Forecasting Model for Large Power System Using GRU-Attention Algorithm," Energies, MDPI, vol. 18(13), pages 1-22, June.
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