Short-Term Wind Power Forecasting Based on OMNIC and Adaptive Fractional Order Generalized Pareto Motion Model

Fan Cai, Dongdong Chen (), Yuesong Jiang and Tongbo Zhu
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Fan Cai: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Dongdong Chen: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Yuesong Jiang: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Tongbo Zhu: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China

Energies, 2024, vol. 17, issue 23, 1-20

Abstract: With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive fractional Generalized Pareto motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results show that the adaptive fGPm model reduces RMSE by 0.448 MW and 0.466 MW, MAPE by 6.936% and 9.702%, and achieves an average R 2 of 0.9826 compared to CNN-GRU and CNN-LSTM. The improvement is due to the dynamic adjustment to data trends and effective use of LRD features. This method provides practical value in improving wind power prediction accuracy and addressing grid integration and regulation challenges.

Keywords: wind power forecasting; orthogonalized maximal information coefficient; adaptive fractional generalized pareto motion model; LRD; uncertainty modeling (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: 2024
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Citations: View citations in EconPapers (1)

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