A review of enhancing wind power with AI: applications, economic implications, and green innovations

Abstract Wind energy, a renewable resource characterized by its inexhaustibility and absence of pollutants, has garnered significant attention in recent years. The optimization of wind power generation for both economic and environmental benefits has emerged as a solution to contemporary energy challenges. Artificial intelligence (AI), particularly machine learning (ML), enhances the efficiency and sustainability of power generation in wind energy systems. This study employs a systematic literature review (SLR) methodology to examine the relevant literature. The findings indicate that AI, predominantly represented by ML and hybrid AI models, contributes to wind energy systems in three primary domains: first, the forecasting and analysis of variables, second the optimization of wind turbines (WTs) performance through advanced maintenance management and condition monitoring, and finally wind farm layout and optimization. Subsequently, we discussed how AI facilitates optimizes employment and energy consumption structures, promotes the green transformation of wind power enterprises, and drives innovation in the wind power industry through wind variable forecasting and turbine maintenance. The application of AI in the wind energy domain presents opportunities for restructuring the energy landscape. Efforts could be made to accelerate AI-driven innovation in the renewable energy sector and promote transformative reorganization of the energy industry.