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September 16, 2025Open Access

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

Key Points

The final model achieved an MAPE of 0.890%, enhancing predictive accuracy for power systems.
Incorporating Kalman filter-based pseudo-trend components lowered forecasting error by 0.834 percentage points.
The proposed model incorporates renewable generation effects and key weather variables for better predictions.
Over a six-hour horizon, this method effectively addresses the challenges of load variability in power systems.

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.

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

Key Points

Abstract

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