Artificial Intelligence in Renewable Energy Systems: A Comprehensive Review of Applications, Challenges, and Future Directions

Abstract

Renewable energy technologies have been the centre of research and development globally, due to the fast evolution on clean and sustainable energy. Although renewable energy is beneficial for the environment, it can be impacted by changes in weather, geographic constraints, or complexity in operational use, as demonstrated by solar, wind, hydropower, and biomass sources. All of these factors pose major difficulties in the prediction of energy use, system optimization and resource management. Artificial intelligence (AI) has proven to be a potent solution to some of these problems. AI can predict renewable energy consumption with greater accuracy, enhance system performance, and increase automation in renewable energy systems, to name a few. The aim of this paper is to provide an overview of the latest studies published from 2020 to 2026, and to review the ways in which AI has been applied in each of the renewable energy sectors. The review gives insight into the main topics such as solar irradiance forecasting, wind speed prediction, fault diagnosis of PV systems, optimization of perovskite solar cells, smart grid load management, and hydropower flow forecasting. It also comparatively analyzes the effectiveness, scalability and applicability of popular AI models like: artificial neural networks, long short term memory networks, convolutional neural networks, support vector machines, random forests, and reinforcement learning algorithms. The paper also explores the challenges currently facing the wider application of AI in renewable energy systems, such as the scarcity of data, challenges with model interpretation, and the heightened threat of cybersecurity breaches. Lastly, it identifies some exciting research areas including physics informed neural networks, generative AI, and digital twin technologies. Perovskite solar cells are one such promising avenue for the discovery of novel materials and advancement of renewable energy via artificial intelligence.