Artificial Intelligence and Machine Learning for Solar Thermal System Design

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.