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Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach

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  • Yang, Dongchuan
  • Guo, Ju-e
  • Li, Yanzhao
  • Sun, Shaolong
  • Wang, Shouyang

Abstract

Short-term load forecasting has evolved into an important aspect of power system in safe operation and rational dispatching. However, given the load series' instability and volatility, this is a challenging task. To this end, this study proposes a dynamic decomposition-reconstruction-ensemble approach by cleverly and dynamically combining two proven and effective techniques (i.e., the reconstruction techniques and the secondary decomposition techniques). In fact, by introducing the decomposition-reconstruction process based on the dynamic classification, filtering, and giving the criteria for determining the components that need to be decomposed again, our proposed model improves the decomposition-ensemble forecasting framework. Our proposed model makes full use of decomposition techniques, complexity analysis, reconstruction techniques, secondary decomposition techniques, and a neural network optimized by an automatic hyperparameter optimization algorithm. Besides, we compared our proposed model with state-of-the-art models including five models with reconstruction techniques and two models with secondary decomposition techniques. The experiment results demonstrate the superiority of our proposed dynamic decomposition-reconstruction technique in terms of forecasting accuracy, precise direction, equality, stability, correlation, comprehensive accuracy, and statistical tests. To conclude, our proposed model has the potential to be a useful tool for short-term load forecasting.

Suggested Citation

  • Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222024951
    DOI: 10.1016/j.energy.2022.125609
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    1. Wang, Delu & Tian, Cuicui & Mao, Jinqi & Chen, Fan, 2023. "Forecasting coal demand in key coal consuming industries based on the data-characteristic-driven decomposition ensemble model," Energy, Elsevier, vol. 282(C).

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