A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges

Zongxu Liu, Hui Guo, Yingshuai Zhang and Zongliang Zuo ()
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Zongxu Liu: Henan Jiuyu Enpai Power Technology Co., Ltd., Zhengzhou 450001, China
Hui Guo: Henan Jiuyu Enpai Power Technology Co., Ltd., Zhengzhou 450001, China
Yingshuai Zhang: Henan Jiuyu Enpai Power Technology Co., Ltd., Zhengzhou 450001, China
Zongliang Zuo: School of Environmental and Municipal Engineering, Qingdao University of Technology, No. 777, Jialingjiang East Rd., Qingdao 266520, China

Energies, 2025, vol. 18, issue 2, 1-17

Abstract: Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios.

Keywords: wind power prediction; machine learning; deep learning; renewable energy; forecasting; model optimization (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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