Artificial Intelligence and Machine Learning for Solar Thermal System Design

Advances, Challenges, and Applications
1. Auflage 2027
ISBN: 978-1-041-34320-2
Verlag: Taylor & Francis Ltd

Buch, Englisch, 368 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Artificial Intelligence and Machine Learning for Intelligent Engineering Systems

The text provides a comprehensive exploration of the advancements in solar thermal technology, focusing on both fundamental processes and their practical applications across industries. It explores cutting-edge innovations in the field, highlighting novel materials, advanced collector designs, and breakthrough storage techniques that promise to enhance the efficiency and scalability of solar thermal systems.

- Focuses on solar thermal energy, covering solar thermal collectors, high-temperature solar power plants, and thermochemical material selection.

- Discusses artificial intelligence, machine learning for solar thermal system design, LLMs, predictive maintenance and early fault detection in solar thermal systems.

- Emphasizes the use of artificial intelligence and machine learning for system design, energy management, performance optimization, and grid integration in solar thermal systems.

- Explores techno-economic analysis, lifecycle economic and environmental impacts of solar thermal energy conversion systems.

- Covers solar thermal energy in food processing industry, and case studies of solar thermal energy in emerging markets.

It is primarily written for senior undergraduates, graduate students, and academic researchers in mechanical engineering, computer science engineering, energy engineering, environmental engineering, solar energy, and renewable energy.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Sharma, Naveen

Kumar, Ashwani

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau

Inhaltsverzeichnis

Section I. Fundamentals of Solar Thermal Systems & Component Design. Chapter 1. Introduction to Solar Thermal Energy: Principles and Applications. Chapter 2. Solar Thermal Collectors: Design, Materials, and Applications. Chapter 3. High-Temperature Solar Thermal Power Plants: Configuration, Structural Challenges, and Design Scalability. Chapter 4. Thermal Energy Storage for Solar Applications: Sensible, Latent, and Thermochemical Material Selection. Chapter 5. Thermal Energy Storage for Solar Applications. Section II. AI & Machine Learning Frameworks for Solar Design. Chapter 6. AI and Machine Learning for Solar Thermal System Design: Theoretical Frameworks and Core Methodologies. Chapter 7. AI and Machine Learning for Solar Thermal System Design. Chapter 8. Performance and Reasoning Benchmarking of Custom-Built Large Language Models (LLMs) Against Contemporary AI Systems in Renewable Energy Engineering. Chapter 9. AI and Machine Learning for Predictive Maintenance and Early Fault Detection in Solar Thermal Systems: Frameworks and Core Methodologies. Section III. Advanced Control, Energy Management, and Grid Integration. Chapter 10. Machine Learning-Assisted Model Predictive Control for a PV/T-Based Solar Thermal Crop Drying. Chapter 11. Machine Learning-based imitation of Sliding Mode Control for Grid-Connected Solar PV Inverters. Chapter 12. Energy Management and Grid Integration with AI. Chapter 13. AI-Driven Performance Optimization in Hybrid Solar Systems. Chapter 14. Bidirectional Energy Flow Management using Artificial Intelligence in Vehicle-to-Grid (V2G) Systems: A Detail Systematic Study. Section IV. Techno-Economic Feasibility, Policy, and Sustainability. Chapter 15. Techno-Economic Analysis and Lifecycle Environmental Impacts of Solar Thermal Energy Conversion Systems. Chapter 16. Economic & Environmental Impacts of Solar Thermal Energy. Chapter 17. Case Studies of Solar Thermal Energy in Emerging Markets. Section V. Multi-Disciplinary AI Applications & Case Studies. Chapter 18. Solar Thermal Energy in Food Processing Industry. Chapter 19. Optimization-Based Approaches for Task Scheduling to Enhance Resource Utilization in Cloud Computing: A Review. Chapter 20. Treatment of Industrial Wastewater from the Bakery Sector Using Solar-Assisted Sequencing Batch Reactors and AI Water Quality Modeling. Chapter 21. Deep Learning-Based Classification of Satellite Images for Risk Assessment of Infrastructure in the Landslide-Prone Uttarakhand Hilly Region. Chapter 22. Performance Analysis of a Deep Learning-Based Human Activity Recognition System Using CNN-LSTM Model.

Über Autor(innen)

Naveen Sharma is currently serving as an Assistant Professor in the Department of Mechanical Engineering, Netaji Subhas University of Technology, New Delhi, India. His research interests include Experimental Fluid Mechanics, Optical Techniques (PIV and LCT), Heat Transfer Enhancement, Solar Thermal Systems, Artificial Intelligence, Computational Fluid Dynamics and Optimization Techniques.

Ashwani Kumar currently holds the position of Professor & Head, Mechanical Engineering (Gazetted Officer Group A) at the Technical Education Department Uttar Pradesh Kanpur (under Government of Uttar Pradesh), India. Dr. Kumar is actively involved in cutting-edge research, focusing on areas such as Artificial Intelligence and Machine Learning in Mechanical Engineering, Smart Materials and Manufacturing Techniques, Thermal Energy Storage, Building Efficiency, Renewable Energy Harvesting, Sustainable Transportation, and Heavy Vehicle Dynamics.