IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i12p5267-d1673751.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/12/5267/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/12/5267/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
    2. Ji, Ling & Zhang, Bei-Bei & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao, 2018. "GHG-mitigation oriented and coal-consumption constrained inexact robust model for regional energy structure adjustment – A case study for Jiangsu Province, China," Renewable Energy, Elsevier, vol. 123(C), pages 549-562.
    3. Huang, Qian & Li, Jinghua & Zhu, Mengshu, 2020. "An improved convolutional neural network with load range discretization for probabilistic load forecasting," Energy, Elsevier, vol. 203(C).
    4. Badurally Adam, N.R. & Elahee, M.K. & Dauhoo, M.Z., 2011. "Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process," Energy, Elsevier, vol. 36(12), pages 6763-6769.
    5. Sio-Kei Im & Ka-Hou Chan, 2024. "Neural Machine Translation with CARU-Embedding Layer and CARU-Gated Attention Layer," Mathematics, MDPI, vol. 12(7), pages 1-19, March.
    6. Xiao, Wenjing & Mo, Li & Xu, Zhanxing & Liu, Chang & Zhang, Yongchuan, 2024. "A hybrid electric load forecasting model based on decomposition considering fisher information," Applied Energy, Elsevier, vol. 364(C).
    7. Abosedra, Salah & Dah, Abdallah & Ghosh, Sajal, 2009. "Electricity consumption and economic growth, the case of Lebanon," Applied Energy, Elsevier, vol. 86(4), pages 429-432, April.
    8. Tan, Mao & Liao, Chengchen & Chen, Jie & Cao, Yijia & Wang, Rui & Su, Yongxin, 2023. "A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor," Applied Energy, Elsevier, vol. 343(C).
    9. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
    10. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    11. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
    12. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
    13. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiao, Wenjing & Mo, Li & Xu, Zhanxing & Liu, Chang & Zhang, Yongchuan, 2024. "A hybrid electric load forecasting model based on decomposition considering fisher information," Applied Energy, Elsevier, vol. 364(C).
    2. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    3. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    4. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    5. Son, Hyojoo & Kim, Changwan, 2017. "Short-term forecasting of electricity demand for the residential sector using weather and social variables," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 200-207.
    6. Yan, Qin & Lu, Zhiying & Liu, Hong & He, Xingtang & Zhang, Xihai & Guo, Jianlin, 2024. "Short-term prediction of integrated energy load aggregation using a bi-directional simple recurrent unit network with feature-temporal attention mechanism ensemble learning model," Applied Energy, Elsevier, vol. 355(C).
    7. Song, Cairong & Yang, Haidong & Cai, Jianyang & Yang, Pan & Bao, Hao & Xu, Kangkang & Meng, Xian-Bing, 2024. "Multi-energy load forecasting via hierarchical multi-task learning and spatiotemporal attention," Applied Energy, Elsevier, vol. 373(C).
    8. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    9. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    10. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    11. Huang, Yanmei & Hasan, Najmul & Deng, Changrui & Bao, Yukun, 2022. "Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting," Energy, Elsevier, vol. 239(PC).
    12. Liao, Chengchen & Tan, Mao & Li, Kang & Chen, Jie & Wang, Rui & Su, Yongxin, 2024. "Sequence signal prediction and reconstruction for multi-energy load forecasting in integrated energy systems: A bi-level multi-task learning method," Energy, Elsevier, vol. 313(C).
    13. Yue-Xu Li & Qiang Zhou & Xin-Hui Zhang & Jia-Jia Chen & Hao-Dong Wang, 2025. "Mid-Long-Term Power Load Forecasting of Building Group Based on Modified NGO," Energies, MDPI, vol. 18(3), pages 1-22, January.
    14. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
    16. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
    17. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).
    18. Fan, Pengdan & Wang, Dan & Wang, Wei & Zhang, Xiuyu & Sun, Yuying, 2024. "A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM," Energy, Elsevier, vol. 308(C).
    19. Lin, Zhengyang & Lin, Tao & Li, Jun & Li, Chen, 2025. "A novel short-term multi-energy load forecasting method for integrated energy system based on two-layer joint modal decomposition and dynamic optimal ensemble learning," Applied Energy, Elsevier, vol. 378(PA).
    20. Peng, Daogang & Liu, Yu & Wang, Danhao & Zhao, Huirong & Qu, Bogang, 2024. "Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis," Energy, Elsevier, vol. 308(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5267-:d:1673751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.