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Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation

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  • Chen, Yuejiang
  • Xiao, Jiang-Wen
  • Wang, Yan-Wu
  • Luo, Yunfeng

Abstract

Probabilistic forecasting plays an important role in the safety, stability and operation of power system. The traditional quantile regression method of non-parametric probability forecasting has the problem of crossing-quantile. Besides, current neural network methods for wind farm cluster power forecasting often overlook the spatio-temporal correlation among related wind farms. To solve these problems, a cluster power forecasting model (CFM) considering spatio-temporal correlation is proposed in this paper. A novel spatial pattern attention (SPA) combining the advantages of convolutional neural network and attention mechanism is used to extract the spatial information. An improved multi-horizon quantile recurrent neural network (IMQ-RNN) and an improved non-crossing quantile regression (INCQR) strategy are used as the output module of CFM to produce high quality forecasting results. Numerical simulations are conducted by using public real-world data from the Global Energy Forecasting Competition 2014. The results show that the proposed model has excellent performance in both deterministic forecasting and probabilistic forecasting.

Suggested Citation

  • Chen, Yuejiang & Xiao, Jiang-Wen & Wang, Yan-Wu & Luo, Yunfeng, 2025. "Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation," Applied Energy, Elsevier, vol. 377(PA).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pa:s0306261924017392
    DOI: 10.1016/j.apenergy.2024.124356
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