The text covers emerging technologies and innovations in the field, such as hybrid solar thermal systems, advanced materials for collectors, novel heat storage solutions, and advancements in concentrating solar power (CSP) technologies. It presents artificial intelligence and machine learning techniques for solar thermal system optimization.

Key Features:

• Discusses the use of smart technologies such as artificial intelligence and machine learning in transforming solar thermal systems by enhancing their efficiency, reliability, and cost-effectiveness
• Provides insights into optimization techniques for online testing of parameters for solar dryers, AI-based predictive modeling for accurate solar power generation, dual-axis solar panel tracking and monitoring systems, IoT in solar dryer design, and greenhouse-type solar dryer systems
• Covers state-of-the-art computational models used in optimizing solar thermal systems, such as neural networks, genetic algorithms for predictive modeling, control strategies, and system optimization
• Presents case studies and examples of implementations of computational intelligence and smart technologies in solar thermal applications, having real-time applications in a 4kW hybrid solar EV charging station, of five-level inverter for solar photovoltaic systems, agrivoltaic systems, and wind-solar-hydrogen hybrid energy generation
• Introduces simulation and modeling tools used in the design, analysis, and performance evaluation of solar thermal collectors for decentralized cooking, next-generation solar power plants, and hybrid energy generation systems

This book is primarily written for senior undergraduates, graduate students, and academic researchers in mechanical engineering, energy engineering, computer science and engineering, renewable energy, solar energy, and clean technologies.