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| Title: | Artificial Intelligence in Energy & Renewable Energy Systems
|
| Author: | Soteris A Kalogirou (ed) |
| ISBN: | 1600212611 : 9781600212611 |
| Illustrations: | tables & charts |
| Format: | Hardback |
| Size: | 180x260mm |
| Pages: | 471 |
| Weight: | 1.19 Kg. |
| Published: | Nova Science Publishers - May 2007 |
| List Price: | 52.99 Pounds Sterling |
| Availability: | In Print |
| Subjects: | ENERGY TECHNOLOGY & ENGINEERING |
This book presents state of the art applications of artificial intelligence in energy and renewable energy systems design and modelling. It covers such topics as solar energy, wind energy, biomass and hydrogen as well as building services systems, power generation systems, combustion processes and refrigeration. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities dealing with modelling and performance prediction of energy and renewable energy systems.
Preface; Introduction to artificial intelligence technology; Artificial neural networks for meteorological variables pertained to energy and renewable energy applications; Artificial neural networks in solar thermal energy systems; Artificial neural networks and genetic algorithms for the optimisation of solar thermal systems; Artificial neural networks applied in PV systems and solar radiation; Application of control algorithms for wind speed prediction and active power generation; Neuro-fuzzy Hammerstein model based predictive control of a solid oxide fuel cell; The application of fuzzy systems to control and modelling of building systems; Application of computational intelligence techniques to architectural and building acoustics; Active learning strategies for the neural estimation of engine maps; The application of artificial intelligence techniques in combustion systems; Learning control of fluidised-bed combustion processes for power plants; Application of artificial intelligence techniques for the reduction of energy consumption of refrigeration systems; Index.
