Advanced learning techniques for AI in renewable energy

The field of renewable energy is already having a significant effect through artificial intelligence (AI) and especially through deep learning (DL) and ancestral machine learning (ML). This impact has opened a new era of advanced efficiency and sustainability leading to influx of vast number of academicians and industrial experts into the field of development. Modern AI-supported technologies allow controlling, optimizing, predicting, detecting faults and suchlike power-grid stability. These applications directly promote the viability and effectiveness of Renewable Energy Systems (RESs) and are also able to enliven waste-management procedures and better solar-power-plant predictivity observation. There is an array of methods that are AI-driven and have been utilized to optimize the usage of energy through Smart Grid, predict solar, wind, and thermal energy. There are a number of ML and deep learning algorithms that have been designed and implemented in recent years to accomplish the same. This chapter thus explores the realm of AI in the education of renewable energy, focusing on the area of smart infrastructure, wind energy, solar energy, and tidal energy, and energy optimization and predictive maintenance. It also looks into the future of such technologies as far as renewable sources of energy are concerned in terms of opportunity, risk, and associated benefits.