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<p>In the context of increasing electricity consumption and the growing complexity of energy usage patterns, accurate power load forecasting faces more significant challenges. Given the widespread demand for precise load curve prediction, this paper proposes a CEEMDAN-SE-VMD + SelfAttention-TCN Fusion model. Load curves contain rich information, and comprehensive information mining contributes to prediction accuracy. Specifically, the methodology first employs Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the load curve, where the resulting Intrinsic Mode Functions (IMFs) are adaptively categorized into high and low-frequency components using Sample Entropy (SE). The high-frequency components undergo further extraction through Variational Mode Decomposition (VMD), while the low-frequency components are directly utilized as model inputs without additional processing. Through this process, the original load curve is reconstructively expressed as sequences of high and low-frequency components. These reconstructed sequences are then fed into Self-Attention Temporal Convolutional Network (TCN) for prediction, and the individual predictions are integrated to generate the final forecast. Using historical German power load data from ENTSO for case analysis, the model achieves an Root Mean Square Error (RMSE) of 24.1293 (MW), Mean Absolute Error (MAE) of 17.268 (MW), and R-square of 0.9838, surpassing the prediction accuracy of other comparative models. Experimental results reveal that the signal decomposition and reconstruction process enables more effective expression of deep characteristic information inherent in load data, thereby enhancing model learning performance. Furthermore, the self-attention mechanism strengthens TCN’s ability to capture data dependencies. The proposed model demonstrates both a high tolerance for raw load data and superior prediction accuracy, with experimental validation confirming its excellent performance in real-world short-term power load forecasting applications.</p>

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Research on load forecasting based on CEEMDAN SE VMD and SelfAttention TCN fusion model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月25日

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  • Haotong Han,
  • Jishen Peng,
  • Jun Ma,
  • Shang Lin Liu,
  • Hao Liu

摘要

In the context of increasing electricity consumption and the growing complexity of energy usage patterns, accurate power load forecasting faces more significant challenges. Given the widespread demand for precise load curve prediction, this paper proposes a CEEMDAN-SE-VMD + SelfAttention-TCN Fusion model. Load curves contain rich information, and comprehensive information mining contributes to prediction accuracy. Specifically, the methodology first employs Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the load curve, where the resulting Intrinsic Mode Functions (IMFs) are adaptively categorized into high and low-frequency components using Sample Entropy (SE). The high-frequency components undergo further extraction through Variational Mode Decomposition (VMD), while the low-frequency components are directly utilized as model inputs without additional processing. Through this process, the original load curve is reconstructively expressed as sequences of high and low-frequency components. These reconstructed sequences are then fed into Self-Attention Temporal Convolutional Network (TCN) for prediction, and the individual predictions are integrated to generate the final forecast. Using historical German power load data from ENTSO for case analysis, the model achieves an Root Mean Square Error (RMSE) of 24.1293 (MW), Mean Absolute Error (MAE) of 17.268 (MW), and R-square of 0.9838, surpassing the prediction accuracy of other comparative models. Experimental results reveal that the signal decomposition and reconstruction process enables more effective expression of deep characteristic information inherent in load data, thereby enhancing model learning performance. Furthermore, the self-attention mechanism strengthens TCN’s ability to capture data dependencies. The proposed model demonstrates both a high tolerance for raw load data and superior prediction accuracy, with experimental validation confirming its excellent performance in real-world short-term power load forecasting applications.