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Short-Term Electric Load Probability Forecasting Based on the BiGRU-GAM-GPR Model

Author

Listed:
  • Qizhuan Shao

    (Yunnan Power Grid Co., Ltd., 73# Tuodong Road, Kunming 650011, China)

  • Rungang Bao

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Shuangquan Liu

    (China Southern Power Grid Lancang-Mekong International Co., Ltd., 15 Guangfu Road, Kunming 650228, China)

  • Kaixiang Fu

    (Yunnan Power Grid Co., Ltd., 73# Tuodong Road, Kunming 650011, China)

  • Li Mo

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Wenjing Xiao

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

Abstract

Accurate and reliable short-term electricity load forecasting plays an important role in ensuring the healthy operation of the power grid and promoting sustainable socio-economic development. This research proposes a novel hybrid load probability prediction model, BiGRU-GAM-GPR, which combines a bidirectional gated recurrent unit (BiGRU), global attention mechanism (GAM), and Gaussian process regression (GPR). Firstly, BiGRU-GAM is used to predict the sequence to obtain preliminary prediction results, and then these results are input into GPR to obtain more accurate deterministic and probabilistic prediction results. To verify the effectiveness of the proposed model, a series of experiments are conducted on three real-world power load datasets. The experimental results show the following: (1) BiGRU has the optimal forecasting ability compared with the other basic models. (2) The global attention mechanism improves the model’s perception ability of the spatial features of multi-feature sequences and plays a positive role in enhancing the model’s forecasting performance. (3) The GPR model further explores the internal relationships of the data by expanding the deterministic prediction results into probabilistic results, thus improving the forecasting effect. (4) The proposed model BiGRU-GAM-GPR exhibits the best performance in both deterministic and probabilistic forecasting and has good robustness.

Suggested Citation

  • Qizhuan Shao & Rungang Bao & Shuangquan Liu & Kaixiang Fu & Li Mo & Wenjing Xiao, 2025. "Short-Term Electric Load Probability Forecasting Based on the BiGRU-GAM-GPR Model," Sustainability, MDPI, vol. 17(12), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5267-:d:1673751
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    References listed on IDEAS

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