IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v317y2025ics0360544225001604.html
   My bibliography  Save this article

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics

Author

Listed:
  • Wang, Junjie
  • Huang, Wenyu
  • Ding, Yuanping
  • Dang, Yaoguo
  • Ye, Li

Abstract

The electric power load forecasting has important theoretical and practical significances for balancing electric power supply. To produce the precise values of the future electric power load, a new discrete grey multivariable prediction model with accumulative time-delay effects and periodic fluctuation characteristics is proposed. In the new model, the accumulative time-delay effects and periodic fluctuation are analyzed and represented by the algebraic expressions. The discrete expression of the new model, containing both the accumulative time-delay effects and periodic fluctuation characteristics, is put forward. A theorem is developed to give the matrix representations of the unknown parameters through the least squares estimation. Then, the vector autoregression model is carried out to identify the accumulative time-delay parameters. A nonlinear programming problem is developed to optimize the parameters by using the whale optimization algorithm. Finally, the model is implemented to predict the monthly electricity consumptions in Zhejiang Province to validate its advantages. Results show the new model has better performances than benchmark models. The model decreases the MAPE value by 121.05 %, 32.58 %, 238.35 % and 189.47 % than similar grey models and reduces the MAPE value by 20.05 %, 17.54 %, 285.21 % and 275.69 % than the statistics method models and the machine leaning models.

Suggested Citation

  • Wang, Junjie & Huang, Wenyu & Ding, Yuanping & Dang, Yaoguo & Ye, Li, 2025. "Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics," Energy, Elsevier, vol. 317(C).
    • Handle: RePEc:eee:energy:v:317:y:2025:i:c:s0360544225001604
    DOI: 10.1016/j.energy.2025.134518
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225001604
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134518?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Yonggang & Zhao, Kaixing & Hao, Yue & Yao, Yilin, 2024. "Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory," Applied Energy, Elsevier, vol. 366(C).
    2. Oreshkin, Boris N. & Dudek, Grzegorz & Pełka, Paweł & Turkina, Ekaterina, 2021. "N-BEATS neural network for mid-term electricity load forecasting," Applied Energy, Elsevier, vol. 293(C).
    3. Zhang, Guoxing & Shen, Lin & Su, Bin, 2023. "Temperature change and daily urban-rural residential electricity consumption in northwestern China: Responsiveness and inequality," Energy Economics, Elsevier, vol. 126(C).
    4. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).
    5. Fan, Jingmin & Zhong, Mingwei & Guan, Yuanpeng & Yi, Siqi & Xu, Cancheng & Zhai, Yanpeng & Zhou, Yongwang, 2024. "An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration," Energy, Elsevier, vol. 299(C).
    6. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
    7. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).
    8. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    9. Huang, Jing & Qin, Rui, 2024. "Elman neural network considering dynamic time delay estimation for short-term forecasting of offshore wind power," Applied Energy, Elsevier, vol. 358(C).
    10. Ding, Song & Hu, Jiaqi & Lin, Qianqian, 2023. "Accurate forecasts and comparative analysis of Chinese CO2 emissions using a superior time-delay grey model," Energy Economics, Elsevier, vol. 126(C).
    11. Du, Ruoyun & Chen, Hongfei & Yu, Min & Li, Wanying & Niu, Dongxiao & Wang, Keke & Zhang, Zuozhong, 2024. "3DTCN-CBAM-LSTM short-term power multi-step prediction model for offshore wind power based on data space and multi-field cluster spatio-temporal correlation," Applied Energy, Elsevier, vol. 376(PA).
    12. Liu, Xiaomei & Li, Sihan & Gao, Meina, 2024. "A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast," Energy, Elsevier, vol. 296(C).
    13. Zeng, Bo & He, Chengxiang & Mao, Cuiwei & Wu, You, 2023. "Forecasting China's hydropower generation capacity using a novel grey combination optimization model," Energy, Elsevier, vol. 262(PA).
    14. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).
    15. Ye, Lili & Xie, Naiming & Boylan, John E. & Shang, Zhongju, 2024. "Forecasting seasonal demand for retail: A Fourier time-varying grey model," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1467-1485.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Jinxue & Duan, Pengfei & Cao, Xiaodong & Xue, Qingwen & Zhao, Bingxu & Zhao, Xiaoyu & Yuan, Xiaoyang & Zhang, Chenyang, 2025. "A multi-energy load forecasting method based on the Mixture-of-Experts model and dynamic multilevel attention mechanism," Energy, Elsevier, vol. 324(C).

    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. Wang, Junjie & Ye, Li & Ding, Xiaoyu & Dang, Yaoguo, 2024. "A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation," Energy, Elsevier, vol. 304(C).
    2. Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).
    3. Duan, Tianyao & Guo, Huan & Qi, Xiao & Sun, Ming & Forrest, Jeffrey, 2024. "A novel information enhanced Grey Lotka–Volterra model driven by system mechanism and data for energy forecasting of WEET project in China," Energy, Elsevier, vol. 304(C).
    4. Xia, Lin & Ren, Youyang & Wang, Yuhong & Pan, Yangyang & Fu, Yiyang, 2024. "Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model," Renewable Energy, Elsevier, vol. 233(C).
    5. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    6. Yingrui Chen & Jiarong Shi, 2025. "Broad Random Forest: A Lightweight Prediction Model for Short-Term Wind Power by Fusing Broad Learning and Random Forest," Sustainability, MDPI, vol. 17(11), pages 1-16, May.
    7. Qin, Fuli & Tong, Mingyu & Huang, Ying & Zhang, Yubo, 2024. "Modeling, prediction and analysis of natural gas consumption in China using a novel dynamic nonlinear multivariable grey delay model," Energy, Elsevier, vol. 305(C).
    8. Xia, Lin & Ren, Youyang & Wang, Yuhong & Fu, Yiyang & zhou, Ke, 2024. "A novel dynamic structural adaptive multivariable grey model and its application in China's solar energy generation forecasting," Energy, Elsevier, vol. 312(C).
    9. Gou, Xiaoyi & Mi, Chuanmin & Zeng, Bo, 2025. "Mixed-frequency grey prediction model with fractional lags for electricity demand and estimation of coal power phase-out scale," Energy, Elsevier, vol. 320(C).
    10. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    11. Wu, Wen-Ze & Xu, Jie & Xie, Wanli & Zhang, Tao, 2025. "An innovative fractional grey system model and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 230(C), pages 68-79.
    12. He, Jiaming & Tan, Qinliang & Lv, Hanyu, 2025. "Data-driven climate resilience assessment for distributed energy systems using diffusion transformer and polynomial expansions," Applied Energy, Elsevier, vol. 380(C).
    13. Wang, Yong & Yang, Rui & Sun, Lang & Yang, Zhongsen & Sapnken, Flavian Emmanuel & Li, Hong-Li, 2025. "A novel time-lag discrete grey Euler model and its application in renewable energy generation prediction," Renewable Energy, Elsevier, vol. 245(C).
    14. Ding, Song & Cai, Zhijian & Qin, Xinghuan & Shen, Xingao, 2024. "Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms," Applied Energy, Elsevier, vol. 367(C).
    15. Li, Hui & Nie, Weige & Duan, Huiming, 2024. "A Haavelmo grey model based on economic growth and its application to energy industry investments," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    16. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    17. Wang, Lei & Wang, Xinyu & Zhao, Zhongchao, 2024. "Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression," Energy, Elsevier, vol. 304(C).
    18. Tong, Xi & Zhao, Shuyuan & Chen, Heng & Wang, Xinyu & Liu, Wenyi & Sun, Ying & Zhang, Lei, 2025. "Optimal dispatch of a multi-energy complementary system containing energy storage considering the trading of carbon emission and green certificate in China," Energy, Elsevier, vol. 314(C).
    19. Zhao, Xiaoyu & Duan, Pengfei & Cao, Xiaodong & Xue, Qingwen & Zhao, Bingxu & Hu, Jinxue & Zhang, Chenyang & Yuan, Xiaoyang, 2025. "A probabilistic load forecasting method for multi-energy loads based on inflection point optimization and integrated feature screening," Energy, Elsevier, vol. 327(C).
    20. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(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:eee:energy:v:317:y:2025:i:c:s0360544225001604. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.