Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances

Xiaoyin Xu, Zhumei Luo () and Menglong Feng
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Xiaoyin Xu: Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Zhumei Luo: Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
Menglong Feng: School of Energy Power and Mechanical Engineering, North China Electric Power University (NCEPU), Beijing 102206, China

Energies, 2025, vol. 18, issue 11, 1-19

Abstract: Wind power forecasting is challenging because of complex, nonlinear relationships between inherent patterns and external disturbances. Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose and model intertwined spatio-temporal dependencies. Current methods typically treat wind power as a unified signal without explicitly separating inherent patterns from external influences, so they have limited prediction accuracy. This paper introduces a novel framework GCN-EIF that decouples external interference factors (EIFs) from inherent wind power patterns to achieve excellent prediction accuracy. Our innovation lies in the physically informed architecture that explicitly models the mathematical relationship: P ( t ) = P inherent ( t ) + EIF ( t ) . The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. A key methodological contribution is our signal decomposition strategy during the prediction phase, where an EIF is eliminated from historical data to better learn fundamental patterns, and then a predicted EIF is reintroduced for the target period, significantly reducing error propagation. Extensive experiments across diverse wind farm clusters and different weather conditions indicate that GCN-EIF achieves an 18.99% lower RMSE and 5.08% lower MAE than state-of-the-art methods. Meanwhile, real-time performance analysis confirms the model’s operational viability as it maintains excellent prediction accuracy (RMSE < 15) even at high data arrival rates (100 samples/second) while ensuring processing latency below critical thresholds (10 ms) under typical system loads.

Keywords: wind power forecasting; multi-graph convolution networks; external interference factor; LSTM; attention (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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