Advancements in Wind Power Forecasting: A Comprehensive Review - Kumar, Krishan; Prabhakar, Priti; Verma, Avnesh (Journal of Electronics and Electrical Engineering, 2025)
Accurate wind power forecasting is crucial for integrating wind energy into modern power systems. However, it remains challenging due to wind variability, complex atmospheric dynamics, and local terrain effects. Traditional models often fail to capture these complexities, especially for short-term and intra-hour predictions. This has driven a shift toward machine learning (ML) methods, which offer transformative potential in this field. By leveraging large datasets and advanced algorithms, ML models can identify intricate patterns and significantly improve prediction accuracy. Techniques such as deep learning, ensemble methods, and hybrid approaches integrate weather data with historical power output, enhancing both spatial and temporal resolution. Nevertheless, challenges such as data quality, model interpretability, and computational demands still require further research to fully realize ML's potential in wind forecasting. The global transition toward smart grids, driven by increasing renewable energy penetration, highlights the need for reliable forecasting. As a key renewable source, wind energy helps reduce greenhouse gas emissions and mitigate global warming. However, its stochastic nature complicates power system management. Accurate forecasting is essential for grid security, sustainability, and efficient energy market operations. This review examines ML-based wind power forecasting methods, categorizing them into supervised, unsupervised, semi-supervised, and reinforcement learning techniques. It emphasizes their adaptability, scalability, and real-time capabilities, while also addressing challenges related to noisy data, dynamic system behavior, and complex grid configurations. Hybrid and ensemble models show particular promise in overcoming these obstacles. The paper also summarizes recent advancements in AI-based wind forecasting, covering data preparation, feature selection, and model evaluation. By identifying research gaps and emerging trends, it suggests strategic directions for developing more robust ML-driven forecasting systems. Ultimately, this work underscores the importance of integrating advanced ML techniques to enhance forecasting reliability, support grid management, and promote a sustainable energy future.