Very Short-Term Load Forecasting Model for Large Power System Using GRU-Attention Algorithm

Tae-Geun Kim, 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
Sung-Guk Yoon: Department of Electrical Engineering and Convergence of Energy Policy and Technology, Soongsil University, Seoul 06978, Republic of Korea
Kyung-Bin Song: Department of Electrical Engineering and Convergence of Energy Policy and Technology, Soongsil University, Seoul 06978, Republic of Korea

Energies, 2025, vol. 18, issue 13, 1-22

Abstract: This paper presents a very short-term load forecasting (VSTLF) model tailored for large-scale power systems, employing a gated recurrent unit (GRU) network enhanced with an attention mechanism. To improve forecasting accuracy, a systematic input feature selection method based on Normalized Mutual Information (NMI) is introduced. Additionally, a novel input feature termed the load variationis proposed to explicitly capture real-time dynamic load patterns. Tailored data preprocessing techniques are applied, including load reconstitution to account for the impact of Behind-The-Meter (BTM) solar generation, and a weighted averaging method for constructing representative weather inputs. Extensive case studies using South Korea’s national power system data from 2021 to 2023 demonstrate that the proposed GRU-attention model significantly outperforms existing approaches and benchmark models. In particular, when expressing the accuracy of the proposed method in terms of the error rate, the Mean Absolute Percentage Error (MAPE) is 0.77%, which shows an improvement of 0.50 percentage points over the benchmark model using the Kalman filter algorithm and an improvement of 0.27 percentage points over the hybrid deep learning benchmark (CNN-BiLSTM). The simulation results clearly demonstrate the effectiveness of the NMI-based feature selection and the combination of load characteristics for very short-term load forecasting.

Keywords: deep learning; large power system; load forecasting; machine learning; real-time load forecasting; time-series 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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