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Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Author

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  • Yuqi Dong

    (College of Law, Guangxi Normal University, Guilin 541004, China)

  • Xuejiao Ma

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116023, China)

  • Chenchen Ma

    (School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China)

  • Jianzhou Wang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116023, China
    School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China)

Abstract

Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

Suggested Citation

  • Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1050-:d:85173
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    References listed on IDEAS

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    7. Hao, Yan & Tian, Chengshi, 2019. "A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 238(C), pages 368-383.
    8. Chengshi Tian & Yan Hao, 2018. "A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-34, March.
    9. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).
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