AI-Powered Energy Storage
Technologies, Applications, and Future Directions
- 1st Edition - January 1, 2027
- Latest edition
- Editor: Andaç Batur Çolak
- Language: English
AI-Powered Energy Storage: Technologies, Applications, and Future Directions provides readers with a comprehensive coverage of AI and machine learning applications specif… Read more
Description
Description
AI-Powered Energy Storage: Technologies, Applications, and Future Directions provides readers with a comprehensive coverage of AI and machine learning applications specifically tailored to energy storage systems. Provides readers with a comprehensive overview of cutting-edge technologies from battery storage systems to smart grids. With a focus on real-world applications and case studies, the book bridges the gap between the latest theoretical research and practical implementation, offering readers insights into the future of energy storage in a rapidly evolving technological landscape. Helps readers overcome challenges in energy storage optimization, predictive modeling and system integration with clear demonstrations of how AI-driven solutions can enhance performance, prediction and optimization. Provides actionable insights and tools for researchers and professionals to implement AI-driven solutions in their work, addressing the growing demand for efficient and sustainable energy storage systems. Takes a forward-looking perspective on future trends and directions in this evolving field. Provides industry professionals, researchers and students with a specialized guide to address challenges and implement solutions in energy storage.
Key features
Key features
- ‘AI-Powered Energy Storage: Technologies, Applications, and Future Directions’ explores the transformative potential of artificial intelligence and machine learning in optimizing energy storage systems
- Offers timely, comprehensive, in-depth insights into sophisticated AI and machine learning strategies for optimizing energy storage systems, as well as addressing inefficiencies in performance prediction and integration
- Is a novel integration of artificial intelligence and energy storage technologies, addressing the critical demand for innovative solutions in a fast growing industry sets the book apart from many other works which generally focus either on AI or energy technology
- Presents a thorough review of advanced AI and machine learning techniques applicable to energy storage systems, insights into the most recent technologies and innovations in the field, and practical guidance on integrating these solutions into existing energy frameworks
Readership
Readership
Academic researchers and professionals in energy science, materials engineering, and data-driven applications; senior undergraduates, postgraduates and PhD students specializing in energy storage, machine learning and AI applications in energy systems; industry professionals working in renewable energy, battery technology and smart grid development
Table of contents
Table of contents
1. Introduction to Energy Storage Technologies and AI
2. Fundamentals of Machine Learning for Energy Systems
3. AI-Driven Modeling and Optimization of Battery Systems
4. Predictive Analytics for Energy Storage Performance
5. Integration of AI with Smart Grids and Renewable Energy
6. AI in Thermal and Mechanical Energy Storage
7. AI for Hydrogen and Fuel Cell Storage Systems
8. Case Studies: AI Applications in Energy Storage
2. Fundamentals of Machine Learning for Energy Systems
3. AI-Driven Modeling and Optimization of Battery Systems
4. Predictive Analytics for Energy Storage Performance
5. Integration of AI with Smart Grids and Renewable Energy
6. AI in Thermal and Mechanical Energy Storage
7. AI for Hydrogen and Fuel Cell Storage Systems
8. Case Studies: AI Applications in Energy Storage
Product details
Product details
- Edition: 1
- Latest edition
- Published: January 1, 2027
- Language: English
About the editor
About the editor
AÇ
Andaç Batur Çolak
Andaç Batur Çolak, PhD, is an Associate Professor in the Department of Information Systems and Technologies, Faculty of Computer and Information Sciences, at Niğde Ömer Halisdemir University, Türkiye. His research focuses on artificial intelligence, machine learning, intelligent systems, neural modeling, energy technologies, and engineering optimization. His work explores the application of computational and data-driven methods to engineering and technology challenges, with particular interests in AI-enabled modeling, optimization, and decision-support systems. He collaborates on interdisciplinary research spanning computer science, engineering, and energy-related applications. Dr. Çolak is affiliated with research and academic activities in the fields of artificial intelligence and intelligent systems and contributes to the development of computational approaches for complex engineering problems.
Affiliations and expertise
Niğde Ömer Halisdemir University, Turkey