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

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

Applied Energy, 2025, vol. 377, issue PA, No S0306261924017392

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.

Keywords: Probabilistic forecasting; Spatio-temporal correlation; Non-crossing quantile regression; Wind power (search for similar items in EconPapers)
Date: 2025
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2024.124356

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