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.

This book:

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 Modelling for Accurate Solar Power Generation, Dual Axis Solar Panel Tracking and Monitoring System, 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, and 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 4KW Hybrid Solar EV Charging Station, of 5-Level Inverter for Solar Photovoltaic Systems, Agrivoltaic system, and Wind Solar Hydrogen Hybrid Energy Generation.

Introduce 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 System.

It 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.