Research on Physically Constrained VMD-CNN-BiLSTM Wind Power Prediction

Yongkang Liu, Yi Gu, Yuwei Long, Qinyu Zhang, Yonggang Zhang () and Xu Zhou ()
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Yongkang Liu: College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
Yi Gu: College of Civil and Architectural Engineering, North China University of Science and Technology, Tangshan 063210, China
Yuwei Long: College of Civil and Architectural Engineering, North China University of Science and Technology, Tangshan 063210, China
Qinyu Zhang: College of Science, North China University of Science and Technology, Tangshan 063210, China
Yonggang Zhang: College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
Xu Zhou: College of Science, North China University of Science and Technology, Tangshan 063210, China

Sustainability, 2025, vol. 17, issue 3, 1-21

Abstract: Accurate forecasting of wind power is crucial for addressing energy demands, promoting sustainable energy practices, and mitigating environmental challenges. In order to improve the prediction accuracy of wind power, a VMD-CNN-BiLSTM hybrid model with physical constraints is proposed in this paper. Initially, the isolation forest algorithm identifies samples that deviate from actual power outputs, and the LightGBM algorithm is used to reconstruct the abnormal samples. Then, leveraging the variational mode decomposition (VMD) approach, the reconstructed data are decomposed into 13 sub-signals. Each sub-signal is trained using a CNN-BiLSTM model, yielding individual prediction results. Finally, the XGBoost algorithm is introduced to add the physical penalty term to the loss function. The predicted value of each sub-signal is taken as the input to get the predicted result of wind power. The hybrid model is applied to the 12 h forecast of a wind farm in Zhangjiakou City, Hebei province. Compared with other hybrid forecasting models, this model has the highest score on five performance indicators and can provide reference for wind farm generation planning, safe grid connection, real-time power dispatching, and practical application of sustainable energy.

Keywords: wind power prediction; sustainability; IForest; VMD; CNN; BiLSTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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