Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC

Yan Yan, Yong Qian and Yan Zhou ()
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Yan Yan: State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
Yong Qian: State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China
Yan Zhou: School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China

Energies, 2025, vol. 18, issue 7, 1-18

Abstract: Accurate forecasting is crucial for enhancing the flexibility and controllability of power grids. Traditional forecasting methods mainly focus on modeling based on a single data source, which leads to an inability to fully capture the underlying relationships in wind power data. In addition, current models often lack dynamic adaptability to data characteristics, resulting in lower prediction accuracy and reliability under different time periods or weather conditions. To address the aforementioned issues, an ultra-short-term hybrid probabilistic prediction model based on MultiFusion, ChronoNet, and adaptive Monte Carlo (AMC) is proposed in this paper. By combining multi-source data fusion and a multiple-gated structure, the nonlinear characteristics and uncertainties of wind power under various input conditions are effectively captured by this model. Additionally, the AMC method is applied in this paper to provide comprehensive, accurate, and flexible ultra-short-term probabilistic predictions. Ultimately, experiments are conducted on multiple datasets, and the results show that the proposed model not only improves the accuracy of deterministic prediction but also enhances the reliability of probabilistic prediction intervals.

Keywords: wind power; ultra-short term; MultiFusion; ChronoNet; adaptive Monte Carlo; probabilistic prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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