AI for Green and Sustainable Energy

This book presents a comprehensive synthesis of how artificial intelligence can accelerate the transition to green and sustainable energy across production, transmission, and consumption. It bridges theory and real-world applications, showcasing AI techniques such as machine learning, optimization, and intelligent control to address energy challenges like renewable integration, energy efficiency, smart-grid operations, and policy formulation. The book features chapters focusing on clean energy solutions for resilient sustainability, designing energy-saving smart homes and IoT-enabled EV charging stations, advanced forecasting techniques for solar radiation and wind energy harvesting, industrial optimization of green biomass fermentation for fuel production, and opportunities and challenges in renewable energy for sustainable development.

Key Features:

• Explores AI applications in solar and wind forecasting, biomass and bioenergy processes, storage, demand-side flexibility, smart homes, EV charging, and waste-to-energy pathways.
• Highlights core AI techniques, including supervised and unsupervised learning, time-series forecasting, reinforcement learning, and optimization.
• Discusses data quality, model validation, interpretability, resilience to cyber-physical threats, and alignment with markets, regulations, and lifecycle assessments.
• Includes everyday examples like smart home energy management and renewable energy forecasting for grid optimization.

Written by international experts, this book is ideal for academics, AI specialists, energy engineers, researchers, and students seeking practical insights into AI applications for green and sustainable energy.