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Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer

Author

Listed:
  • Shichao Huang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Jing Zhang

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Yu He

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Xiaofan Fu

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Luqin Fan

    (College of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Gang Yao

    (Guizhou Power Grid Company, Guiyang 550001, China)

  • Yongjun Wen

    (Pujiang Guangyuan Power Construction Co., Ltd., Pujiang, Jinhua 322200, China)

Abstract

Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Transformer model was 1.1317%, while the RMSE was 304.40, which was better than the selected six forecasting models.

Suggested Citation

  • Shichao Huang & Jing Zhang & Yu He & Xiaofan Fu & Luqin Fan & Gang Yao & Yongjun Wen, 2022. "Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer," Energies, MDPI, vol. 15(10), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3659-:d:817187
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    References listed on IDEAS

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    Cited by:

    1. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    2. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
    3. Shi, Jian & Teh, Jiashen, 2024. "Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion," Applied Energy, Elsevier, vol. 353(PB).

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