A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges
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 the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/350 |
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| author | Zongxu Liu Hui Guo Yingshuai Zhang Zongliang Zuo |
| author_facet | Zongxu Liu Hui Guo Yingshuai Zhang Zongliang Zuo |
| author_sort | Zongxu Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c21ce94252c5433488083cd6764fa7e5 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-c21ce94252c5433488083cd6764fa7e52025-01-24T13:31:09ZengMDPI AGEnergies1996-10732025-01-0118235010.3390/en18020350A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and ChallengesZongxu Liu0Hui Guo1Yingshuai Zhang2Zongliang Zuo3Henan Jiuyu Enpai Power Technology Co., Ltd., Zhengzhou 450001, ChinaHenan Jiuyu Enpai Power Technology Co., Ltd., Zhengzhou 450001, ChinaHenan Jiuyu Enpai Power Technology Co., Ltd., Zhengzhou 450001, ChinaSchool of Environmental and Municipal Engineering, Qingdao University of Technology, No. 777, Jialingjiang East Rd., Qingdao 266520, ChinaWind 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.https://www.mdpi.com/1996-1073/18/2/350wind power predictionmachine learningdeep learningrenewable energyforecastingmodel optimization |
| spellingShingle | Zongxu Liu Hui Guo Yingshuai Zhang Zongliang Zuo A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges Energies wind power prediction machine learning deep learning renewable energy forecasting model optimization |
| title | A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges |
| title_full | A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges |
| title_fullStr | A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges |
| title_full_unstemmed | A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges |
| title_short | A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges |
| title_sort | comprehensive review of wind power prediction based on machine learning models applications and challenges |
| topic | wind power prediction machine learning deep learning renewable energy forecasting model optimization |
| url | https://www.mdpi.com/1996-1073/18/2/350 |
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