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Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model

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  • Yan Hong

    (State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China
    School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
    School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Ding Wang

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Jingming Su

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Maowei Ren

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Wanqiu Xu

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Yuhao Wei

    (School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

  • Zhen Yang

    (State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China
    School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China)

Abstract

Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically seasonality, trend, and residuals), which makes forecasting a challenge. To reduce the volatility of the power load sequence and fully explore the important information within it, a three-stage short-term power load forecasting model based on CEEMDAN-TGA is proposed in this paper. Firstly, the power load dataset is divided into the following three stages: historical data, prediction data, and the target stage. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) decomposition is applied to the first- and second-stage load sequences, and the reconstructed intrinsic mode functions (IMFs) are classified based on their permutation entropies to obtain the error for the second stage. After that, the TCN (temporal convolutional network), GRU (gated recurrent unit), and attention mechanism are combined in the TGA model to predict the errors for the third stage. The third-stage power load sequence is predicted by employing the TGA model in conjunction with the extracted trend features from the first and second stages, as well as the seasonal impact features. Finally, it is merged with the error term. The experimental results show that the forecast performance of the three-stage forecasting model based on CEEMDAN-TGA is superior to those of the TCN-GRU and TCN-GRU-Attention models, with a reduction of 42.77% in MAE, 46.37% in RMSE, and 45.0% in MAPE. In addition, the R 2 could be increased to 0.98. It is evident that utilizing CEEMDAN for load sequence decomposition reduces volatility, and the combination of the TCN and the attention mechanism enhances the ability of GRU to capture important information features and assign them higher weights. The three-stage approach not only predicts the errors in the target load sequence, but also extracts trend features from historical load sequences, resulting in a better overall performance compared to the TCN-GRU and TCN-GRU-Attention models.

Suggested Citation

  • Yan Hong & Ding Wang & Jingming Su & Maowei Ren & Wanqiu Xu & Yuhao Wei & Zhen Yang, 2023. "Short-Term Power Load Forecasting in Three Stages Based on CEEMDAN-TGA Model," Sustainability, MDPI, vol. 15(14), pages 1-28, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11123-:d:1195792
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    References listed on IDEAS

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    1. Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2024. "Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review," Mathematics, MDPI, vol. 12(21), pages 1-51, October.
    2. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.

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