Advanced AI applications in renewable energy systems

Artificial intelligence (AI) is transforming renewable energy systems (RES) by providing innovative solutions that optimize efficiency, enhance reliability, and enable easy integration into next-generation smart grids and energy infrastructures. As global demand for sustainable energy grows and the urgency of mitigating climate change intensifies, renewable energy resources, including solar, wind, biomass, hydropower, and geothermal. These resources have emerged as essential alternatives to fossil fuels. However, the inherent intermittency and variability of these energy sources pose significant challenges for energy management. AI addresses these key challenges by optimizing energy production, improving system forecasting, and ensuring efficient grid integration. AI applications include accurate solar irradiance and wind speed forecasting, biomass combustion optimization, energy storage, fault detection, and predictive maintenance. Despite the great potential, the integration of AI into RES faces challenges such as data quality, computational requirements, and the need for compatibility with existing infrastructure. This chapter provides an overview of the applications of AI in RES. Five types of RES are introduced in this chapter, including solar photovoltaic (PV) systems, wind, hydroelectric, geothermal, and biomass energy systems. It also examines the key AI techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning, and their practical applications in RES. In addition, the chapter describes the challenges, limitations, future trends, and research directions to improve AI models, data collection, and integration for more sustainable and effective energy solutions.