A Modularity-Enhanced Echo State Network for Nonlinear Wind Energy Predicting

Sixian Yue, Zhili Zhao (), Tianyou Lai and Jin Zhang
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Sixian Yue: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
Zhili Zhao: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
Tianyou Lai: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
Jin Zhang: School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China

Energies, 2025, vol. 18, issue 7, 1-19

Abstract: With the rapid growth of wind power generation, accurate wind energy prediction has emerged as a critical challenge, particularly due to the highly nonlinear nature of wind speed data. This paper proposes a modularized Echo State Network (MESN) model to improve wind energy forecasting. To enhance generalization, the wind speed data is first decomposed into time series components, and Modes-cluster is employed to extract trend patterns and pre-train the ESN output layer. Furthermore, Turbines-cluster groups wind turbines based on their wind speed and energy characteristics, enabling turbines within the same category to share the ESN output matrix for prediction. An output integration module is then introduced to aggregate the predicted results, while the modular design ensures efficient task allocation across different modules. Comparative experiments with other neural network models demonstrate the effectiveness of the proposed approach, showing that the statistical RMSE of parameter error is reduced by an average factor of 2.08 compared to traditional neural network models.

Keywords: neural network; wind energy prediction; Echo State Network; modularization; K-means cluster (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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