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Short-term power load forecasting based on the CEEMDAN-TCN-ESN model

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  • Jiacheng Huang
  • Xiaowen Zhang
  • Xuchu Jiang

Abstract

Ensuring an adequate electric power supply while minimizing redundant generation is the main objective of power load forecasting, as this is essential for the power system to operate efficiently. Therefore, accurate power load forecasting is of great significance to save social resources and promote economic development. In the current study, a hybrid CEEMDAN-TCN-ESN forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and higher-frequency and lower-frequency component reconstruction is proposed for short-term load forecasting research. In this paper, we select the historical national electricity load data of Panama as the research subject and make hourly forecasts of its electricity load data. The results show that the RMSE and MAE predicted by the CEEMDAN-TCN-ESN model on this dataset are 15.081 and 10.944, respectively, and R2 is 0.994. Compared to the second-best model (CEEMDAN-TCN), the RMSE is reduced by 9.52%, and the MAE is reduced by 17.39%. The hybrid model proposed in this paper effectively extracts the complex features of short-term power load data and successfully merges subseries according to certain similar features. It learns the complex and varying features of higher-frequency series and the obvious regularity of the lower-frequency-trend series well, which could be applicable to real-world short-term power load forecasting work.

Suggested Citation

  • Jiacheng Huang & Xiaowen Zhang & Xuchu Jiang, 2023. "Short-term power load forecasting based on the CEEMDAN-TCN-ESN model," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-26, October.
    • Handle: RePEc:plo:pone00:0284604
    DOI: 10.1371/journal.pone.0284604
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    References listed on IDEAS

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    5. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
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