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.
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 and energy policy makers and analysts.
1. Introduction and Overview of Current Energy Challenges and the
Transition to Renewable Energy Sources.
2. Renewable Energy Technologies and
Thermal Energy Storage Systems.
3. Low, Medium, and High Temperatures Thermal
Energy Storage (TES) Methods.
4. Research Status and Development Trends on
Cold Storage Technology and its Engineering Applications.
5. Fundamentals of
AI in Energy Storage Applications.
6. Sustainable Synergy with AI and Energy
Storage.
7. Digital Twin Solutions for Heat Storage Applications.
8.
Real-World Case Studies of AI Integration with Thermal Energy Storage
Systems.
9. Future Trends and Challenges in AI-Powered Energy Management
Systems.
Dr. Naveed Ahmed is an Associate Professor at the USPakistan Center for Advanced Studies in Energy, National University of Sciences and Technology (NUST), Islamabad. He is an accomplished researcher in energy technologies with over 42 peer-reviewed journal publications, several book chapters, and a strong record of externally funded research projects. He has actively contributed to leading international conferences in the field of energy systems. With more than ten years of combined postdoctoral and industrial experience, Dr. Ahmed has established himself as a recognized academic contributing to advancements in renewable energy and thermal energy storage research.
Dr. Mumtaz A. Qaisrani is a Tenured Assistant Professor, specializing in Energy Engineering with a focus on renewable energy, Solar and wind systems, and energy efficiency. His publication record includes 40+ papers in SCI journals; he has reviewed over 50 manuscripts for various publishers and is an editor of an academic journal. His diverse academic background and extensive experience drive holistic research advancements in Energy Engineering.
