Machine learning approaches for wind power forecasting: a comprehensive review

The dependable integration of wind energy into contemporary electrical systems, which supports market operations, grid stability, and strategic planning, depends on accurate wind power forecasting. A thorough analysis of machine learning (ML) techniques for wind power prediction is presented in this research, encompassing advancements from 2006 to 2025. Physical, statistical, traditional machine learning, deep learning, ensemble, and hybrid models are the categories into which current forecasting techniques fall. With particular focus on sophisticated designs like Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and hybrid CNN–LSTM frameworks, we examine their capabilities, constraints, and application domains. The paper also looks at lightweight solutions that allow deployment on low-cost edge devices, as well as optimization algorithms like the Fruit Fly Algorithm and Particle Swarm Optimization (PSO). Comparative analyses show how well various models handle temporal dependencies, nonlinearity, scalability, and interpretability over a range of predicting horizons. There is a thorough discussion of the main obstacles, including problems with data quality, computing limitations, and performance in harsh weather. The findings indicate that in order to improve prediction accuracy, robustness, and real-time application, future research should concentrate on IoT-enabled sensor networks, multi-model fusion, physics-informed learning, and sophisticated structures like Transformers. All things considered, this paper charts the developing field of machine learning-driven wind power forecasting and offers practical guidance for developing intelligent, efficient, and sustainable renewable energy systems.