The Application of Wind Power Prediction Based on the NGBoost–GRU Fusion Model in Traffic Renewable Energy System

Fudong Li, Yongjun Gan () and Xiaolong Li
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Fudong Li: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
Yongjun Gan: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
Xiaolong Li: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Sustainability, 2025, vol. 17, issue 14, 1-28

Abstract: In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems.

Keywords: NGBoost–GRU fusion model; wind power probability prediction; electric vehicle dynamic charging strategy; cross-energy fusion optimization (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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