Renewable energy generation is crucial for sustainability but its inconsistency in availability requires better storage solutions. The fusion of renewable energy technologies with AI can yield positive outcomes, such as increased energy efficiency, reduced carbon emissions, improved grid stability, and optimized storage techniques and methods. This book provides a state-of-the-art perspective on thermal energy storage (TES) technologies integrated with renewable energy technologies and Artificial Intelligence (AI). It includes various case studies and practical applications that offer actionable insights for professionals and researchers in the field and addresses real-world challenges. Features Provides a comprehensive overview of thermal energy storage methods and systems. Explains AI's role in enhancing thermal energy storage. Includes real-world case studies and discusses perspectives of how TES and AI contribute to sustainability. Addresses the integration of AI with digital twin technology, showcasing how AI algorithms can enhance the predictive accuracy and operational efficiency of digital twins in TES applications. Explores various AI-driven strategies and technologies that enhance the efficiency, reliability, and scalability of energy storage systems. This book is for researchers, academics, and graduate students in Energy and Environmental Sciences, and those interested in renewable energy, AI, and energy storage. It's also an excellent reference for industry and government professionals, energy policy makers, and analysts.
This book provides a state-of-the-art perspective on thermal energy storage (TES) technologies integrated with renewable energy technologies and Artificial Intelligence (AI). It includes case studies and practical applications that offer actionable insights for professionals and researchers in the field and addresses real-world challenges.
