Overview

The issue of intermittency, or variations in solar irradiance caused by weather, time of day, and geographic considerations, confronts the solar energy industry. Because of this unpredictability, precise forecasting and effective management of solar power generation are essential for a steady supply of energy. Simultaneously, artificial intelligence (AI) approaches, in particular machine learning (ML), deep learning (DL), and neural networks, have shown promise in resolving intricate, nonlinear issues across a range of areas. However, the utilization of these technologies for projecting solar irradiance and optimizing energy management is yet to be explored in depth, necessitating specific skills and methods to properly tap into their potential. AI-Driven Solutions for Solar Energy Efficiency, Irradiance Modeling, and PV Forecasting examines the relationship between solar energy and AI, with a particular emphasis on how AI-driven methods can improve solar power systems' performance, efficiency, and forecasting. It illustrates how AI-based optimization algorithms may maximize energy output and reduce losses in photovoltaic (PV) systems and solar power plants. Covering topics such as charge management, microgrids, and smart building designs, this book is an excellent resource for engineers, executives, policymakers, technologists, environmental advocates, business leaders, investors, professionals, researchers, scholars, academicians, and more.

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Prashant Upadhyay is currently affiliated with Sharda University.

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