Najib El Ouanjli – författare

This book addresses and disseminates state-of-the-art research and development in the applications of soft computing techniques for renewable energy systems. It covers topics such as solar energy, wind energy, and solar concentrator technologies, as well as building systems and power generation systems. In all these areas, applications of soft computing methods such as artificial neural networks, genetic algorithms, particle swarm optimization, cuckoo search, fuzzy logic, and a combination of these, called hybrid systems, are included. This book is a source for students interested in the fields of renewable energy and the application of the soft computing. In addition, our book can be considered as a reference for researchers and academics since it will include applications of soft computing in different renewable energy systems.

This book addresses and disseminates state-of-the-art research and development in the applications of soft computing techniques for renewable energy systems. It covers topics such as solar energy, wind energy, and solar concentrator technologies, as well as building systems and power generation systems. In all these areas, applications of soft computing methods such as artificial neural networks, genetic algorithms, particle swarm optimization, cuckoo search, fuzzy logic, and a combination of these, called hybrid systems, are included. This book is a source for students interested in the fields of renewable energy and the application of the soft computing. In addition, our book can be considered as a reference for researchers and academics since it will include applications of soft computing in different renewable energy systems.

This book aims to propose advanced solutions based on artificial intelligence techniques for ECS in order to increase energy efficiency, ensure the safety of the ECS, and to improve the quality of the energy supplied to the grid. The efficiency and quality of the electrical energy produced depends mainly on the structure and efficiency of the control technology developed for the Energy Conversion System (ECS). To improve the performance of ECSs, it is interesting to design control systems that emulate some functions performed by the human brain. Among these interesting functions are self-adaptation, learning, flexibility of operation and planning in the presence of large uncertainties and with minimal information. Based on these aspects, artificial intelligence (AI) techniques can be developed and applied to solve the different control problems of ECSs.

For academics, professionals, practitioners, and graduate students interested in the most recent research on the application of AI in ECS, it is the ideal reference source.