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

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January 15, 2025
Published Date

Research Abstract & Technology Focus

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.
comprehensive review power prediction machine learning
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What is the core focus of the research titled 'A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges'?

This literature focuses on: 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 techniq...

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Yes, highly correlated activity was mapped. An entry titled 'A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges' discusses this: Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy int...

Are there commercial applications of 'A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'Reconstructing fine-scale 3D wind fields with terrain-informed machine learning' discusses this: This study presents a terrain-aware AI framework trained on computational fluid dynamics that reconstructs fine-scale 3D wind fields from coarse we...

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