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

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3229-:d:1683566
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    References listed on IDEAS

    as
    1. 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).
    2. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
    3. Jiang, Yuqi & Gao, Tianlu & Dai, Yuxin & Si, Ruiqi & Hao, Jun & Zhang, Jun & Gao, David Wenzhong, 2022. "Very short-term residential load forecasting based on deep-autoformer," Applied Energy, Elsevier, vol. 328(C).
    4. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    5. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    6. Waqas Ahmad & Nasir Ayub & Tariq Ali & Muhammad Irfan & Muhammad Awais & Muhammad Shiraz & Adam Glowacz, 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine," Energies, MDPI, vol. 13(11), pages 1-17, June.
    7. Spencer Kerkau & Saeed Sepasi & Harun Or Rashid Howlader & Leon Roose, 2025. "Day-Ahead Net Load Forecasting for Renewable Integrated Buildings Using XGBoost," Energies, MDPI, vol. 18(6), pages 1-12, March.
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