The incorporation of renewable energy into worldwide power systems is crucial for attaining sustainable development objectives and alleviating climate change. Nonetheless, renewable sources like solar and wind are intrinsically unpredictable and intermittent, presenting issues for grid stability, energy forecasting, and storage optimisation. Artificial Intelligence (AI) has become a disruptive catalyst in tackling these difficulties by providing sophisticated solutions in prediction, automation, and optimisation throughout the renewable energy value chain. This chapter examines the function of AI in improving renewable energy systems, emphasising its applications in generation forecasting, smart grid administration, predictive maintenance, energy storage, and demand-side optimisation. Machine learning and deep learning algorithms have markedly enhanced forecasting precision, facilitating more dependable integration of renewable resources into power grids. AI-powered smart grids provide real-time equilibrium of supply and demand, whilst predictive maintenance frameworks minimise downtime and operational expenses for solar panels, wind turbines, and hydropower facilities. Moreover, AI optimises energy storage systems by regulating battery cycles and forecasting ideal charging and discharging intervals, thus enhancing efficiency and longevity. Case studies, such as Google DeepMind’s AI-driven wind energy scheduling and India’s AI-enhanced solar forecasts, demonstrate the concrete advantages of AI in renewable integration. The report highlights constraints include substantial capital expenditures, reliance on data, cybersecurity risks, and the necessity for specialised skills. Nonetheless, the potential of AI is vast, with forthcoming prospects in decentralised microgrids, blockchain-facilitated peer-to-peer trading, and hybrid AI-quantum systems. The findings indicate that AI is not just an auxiliary tool but a vital catalyst in expediting the shift to carbon-neutral energy systems. AI significantly aids in attaining global sustainability and energy security objectives by facilitating intelligent, resilient, and adaptive renewable energy systems.
Das et al. (2025) studied this question.