Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/17882
DC Field | Value | Language |
---|---|---|
dc.contributor.editor | Gopalakrishnan, Kasthurirangan | - |
dc.contributor.editor | Khaitan, Siddhartha Kuma | - |
dc.contributor.editor | Kalogirou, Soteris A. | - |
dc.date.accessioned | 2020-02-27T10:38:46Z | - |
dc.date.available | 2020-02-27T10:38:46Z | - |
dc.date.issued | 2011 | - |
dc.identifier.isbn | 9783642221767 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/17882 | - |
dc.description.abstract | 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. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © 2011 Springer-Verlag Berlin Heidelberg | en_US |
dc.subject | Soft computing | en_US |
dc.subject | Renewable energy | en_US |
dc.subject | Cooling systems | en_US |
dc.title | Soft Computing in Green and Renewable Energy Systems | en_US |
dc.type | Book | en_US |
dc.collaboration | Iowa State University | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Environmental Engineering | en_US |
dc.journals | Subscription | en_US |
dc.country | USA | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1007/978-3-642-22176-7 | en_US |
cut.common.academicyear | 2011-2012 | en_US |
item.openairetype | book | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_2f33 | - |
item.languageiso639-1 | en | - |
crisitem.editor.dept | Department of Mechanical Engineering and Materials Science and Engineering | - |
crisitem.editor.faculty | Faculty of Engineering and Technology | - |
crisitem.editor.orcid | 0000-0002-4497-0602 | - |
crisitem.editor.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Βιβλία/Books |
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