Advancements in wind power forecasting: A comprehensive review of artificial intelligence-based approaches

The growing need for energy from renewable sources, along with the unpredictable nature of wind power, has necessitated the development of efficient Wind Power Forecasting (WPF) algorithms. This study addresses the pressing issue of enhancing WPF algorithms in response to the growing demand for renewable energy and the inherent unpredictability of wind power. Over seven years from 2016 to 2023, conducted an exhaustive analysis of 92 research papers, focusing on the integration of Artificial Intelligence (AI) technologies to develop a robust WPF system. The study employs various AI approaches, including Deep Learning (DL), Machine Learning (ML), and neural networks, to predict wind energy generation with higher precision. Our main findings highlight a significant improvement in prediction accuracy, with the AI-based WPF system outperforming traditional methods by an average of 15%, based on a cross-validation of historical data. The integration of AI enables real-time adaptation to changing weather patterns, resulting in a 20% increase in responsiveness compared to conventional forecasting. Moreover, the proposed system optimizes resource allocation, leading to a 10% increase in energy efficiency and improved grid integration. Our results underscore the potential of AI in revolutionizing WPF, offering tangible advancements in accuracy, responsiveness, and resource efficiency. These findings advocate for the widespread adoption of AI-driven WPF systems to enhance the reliability and performance of renewable energy systems, contributing significantly to the global transition towards sustainable energy.