Advancements in Wind Power Forecasting: A Comprehensive Review

Abstract

Accurate wind power forecasting is essential for the seamless integration of wind energy into modern power systems, yet it remains a challenging task due to the inherent variability of wind, complex atmospheric dynamics, and the influence of local terrain. Traditional forecasting models often struggle to capture these complexities, particularly for short-term and intra-hour predictions. This gap necessitates a shift towards machine learning (ML) methodologies, which have emerged as transformative tools in addressing these challenges. By leveraging large datasets and advanced algorithms, ML models can identify intricate patterns and significantly enhance prediction accuracy. Techniques such as deep learning, ensemble methods, and hybrid approaches integrate weather data with historical power output, improving both spatial and temporal resolutions. Despite their promise, challenges such as data quality, model interpretability, and computational demands require further research to fully optimize ML applications in wind power forecasting. The global transition toward smart grids, driven by the increasing penetration of renewable energy sources (RES), underscores the importance of reliable forecasting. Wind energy, as a key RES, plays a pivotal role in reducing greenhouse gas emissions and mitigating global warming. However, the stochastic nature of wind energy complicates power system analysis and management. Accurate forecasting is critical for enhancing power system security, supporting sustainability, and facilitating economic transactions in energy markets. This review examines ML-based methodologies for wind power forecasting, categorizing them into supervised, unsupervised, semi-supervised, and reinforcement learning techniques. It highlights their adaptability, scalability, and real-time capabilities while addressing challenges posed by noisy data, dynamic system behaviors, and complex grid configurations. Hybrid and ensemble models, in particular, demonstrate exceptional potential in overcoming these challenges. Furthermore, this study provides a detailed summary of the latest advancements in wind power forecasting using AI-based approaches. Key aspects such as data preparation, feature selection, and model assessment are explored, offering insights into improving both the precision and efficiency of wind energy prediction algorithms. By identifying research gaps and emerging trends, this review presents strategic directions for the development of innovative ML-driven forecasting methods. Ultimately, the findings underscore the significance of integrating advanced ML techniques to enhance forecasting reliability, support effective grid management, and contribute to a sustainable energy future.