Green Energy Using Machine and Deep Learning

Renewable energy is increasingly being used to minimize the impact of global warming and climate change. Hence, it has become more prevalent in the worldwide, electric energy grid, but enhancing the reliability of renewable‐energy prediction is crucial to power‐system scheduling, operations, and management. However, this is a difficult task due to the inconsistent and unpredictable nature of renewable‐energy data. Numerous approaches have been developed to increase the prediction accuracy of renewable energy, including statistical analysis, physical models, artificial‐intelligence methods, and their hybrids. Among them, the machine learning (ML) and deep learning (DL) approaches have been widely used to discover inherent nonlinear characteristics and high‐level invariant structures in data. This chapter provides a detailed analysis of renewable‐ energy prediction models based on the ML and DL approach in order to investigate their efficiency, reliability, and application potential. Finally, the present research efforts, difficulties, and possible future work of machine‐learning and deep‐learning techniques for green energy are also discussed.