Soft Computing in Green and Renewable Energy Systems

. Ed(s): Gopalakrishnan, Kasthurirangan; Khaitan, Siddhartha Kumar; Kalogirou, Soteris A.

ISBN 10: 3642221750 / ISBN 13: 9783642221750

Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2011

This practical introduction demonstrates how to apply soft computing and hybrid intelligent systems to a plethora of aspects of green and renewable energy systems, including design, modeling, characterization, optimization, and performance prediction. Editor(s): Gopalakrishnan, Kasthurirangan; Khaitan, Siddhartha Kumar; Kalogirou, Soteris A. Series: Studies in Fuzziness and Soft Computing. Num Pages: 319 pages, biography. BIC Classification: THX; UYQ. Category: (P) Professional & Vocational. Dimension: 234 x 157 x 23. Weight in Grams: 701. . 2011. Hardback. . . . . Books ship from the US and Ireland. Codice articolo V9783642221750

Riassunto:

Soft Computing in Green and Renewable Energy Systems provides a practical introduction to the application of soft computing techniques and hybrid intelligent systems for designing, modeling, characterizing, optimizing, forecasting, and performance prediction of green and renewable energy systems. Research is proceeding at jet speed on renewable energy (energy derived from natural resources such as sunlight, wind, tides, rain, geothermal heat, biomass, hydrogen, etc.) as policy makers, researchers, economists, and world agencies have joined forces in finding alternative sustainable energy solutions to current critical environmental, economic, and social issues. The innovative models, environmentally benign processes, data analytics, etc. employed in renewable energy systems are computationally-intensive, non-linear and complex as well as involve a high degree of uncertainty. Soft computing technologies, such as fuzzy sets and systems, neural science and systems, evolutionary algorithms and genetic programming, and machine learning, are ideal in handling the noise, imprecision, and uncertainty in the data, and yet achieve robust, low-cost solutions. As a result, intelligent and soft computing paradigms are finding increasing applications in the study of renewable energy systems. Researchers, practitioners, undergraduate and graduate students engaged in the study of renewable energy systems will find this book very useful.

Dalla quarta di copertina:

Soft Computing in Green and Renewable Energy Systems provides a practical introduction to the application of soft computing techniques and hybrid intelligent systems for designing, modeling, characterizing, optimizing, forecasting, and performance prediction of green and renewable energy systems. Research is proceeding at jet speed on renewable energy (energy derived from natural resources such as sunlight, wind, tides, rain, geothermal heat, biomass, hydrogen, etc.) as policy makers, researchers, economists, and world agencies have joined forces in finding alternative sustainable energy solutions to current critical environmental, economic, and social issues. The innovative models, environmentally benign processes, data analytics, etc. employed in renewable energy systems are computationally-intensive, non-linear and complex as well as involve a high degree of uncertainty. Soft computing technologies, such as fuzzy sets and systems, neural science and systems, evolutionary algorithms and genetic programming, and machine learning, are ideal in handling the noise, imprecision, and uncertainty in the data, and yet achieve robust, low-cost solutions. As a result, intelligent and soft computing paradigms are finding increasing applications in the study of renewable energy systems. Researchers, practitioners, undergraduate and graduate students engaged in the study of renewable energy systems will find this book very useful.

Dati bibliografici

Titolo: Soft Computing in Green and Renewable Energy...
Casa editrice: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Data di pubblicazione: 2011
Legatura: Rilegato
Condizione: New