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

Tae-Geun Kim, Bo-Sung Kwon, Sung-Guk Yoon and Kyung-Bin Song ()
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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
Kyung-Bin Song: Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of Korea

Energies, 2025, vol. 18, issue 18, 1-19

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.

Keywords: deep learning; large power system; pseudo-input; real-time load forecasting; very short-term load forecasting (VSTLF) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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