Optimizing Sustainable Energy Engineering with AI and Machine Learning

The energy engineering sector stands at a critical juncture, with the potential to fundamentally transform the future of the global energy industry. As intelligent technologies become increasingly embedded in modern systems, they offer a vital opportunity to prioritize sustainability and accelerate the transition toward decarbonized energy solutions. Building on these foundations, recent advancements in artificial intelligence (AI) and machine learning are further expanding the possibilities for optimizing renewable energy integration and enabling more adaptive data-driven energy management strategies. Optimizing Sustainable Energy Engineering With AI and Machine Learning provides a comprehensive and forward-looking exploration of how AI and machine learning are transforming modern energy systems. Bridging theory with practical case studies, this book examines how intelligent, data-driven approaches can enhance the efficiency and performance of renewable energy systems and support decarbonization technologies. Covering topics such as artificial neural network modeling, net-zero cities, and waste-to-energy infrastructure, this book is an excellent academic resource for graduate and doctoral students, electrical and mechanical engineers, energy data scientists, machine learning engineers, policymakers, and more.