00000cmm a22000004a 4500 UP-99796217611212163 Buklod 20241015102137.0 m go j cr cn |||aaaaa 241015s2011 gw ||||go||j| ||||||engdd 9783642221767 (eBook) 3642221769 (eBook) (iLib)UPD-00214608339 CSt DMLR rda eng DMLUC QA 76.9 S63 S64 2011eb Soft computing in green and renewable energy systems Kasthurirangan Gopalakrishnan, Siddhartha Kumar Khaitan, Soteris Kalogirou, editors. Berlin Springer 2011. 1 online resource (xiv, 304 pages) illustrations text rdacontent computer rdamedia online resource rdacarrier Studies in fuzziness and soft computing v. 269 Includes bibliographical references and author index From the content: Soft Computing Applications in Thermal Energy Systems -- Use of Soft Computing Techniques in Renewable Energy Hydrogen Hybrid Systems -- Soft Computing in Absorption Cooling Systems -- A Comprehensive Overview of Short Term Wind Forecasting Models based on Time Series Analysis -- Load Flow with Uncertain Loading and Generation in Future Smart Grids IP-based subscription, access limited to within on campus computer network Access via Electronic Resources of the UPD University Library Website 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 Electronic reproduction New York SpringerLink 2011 Available via World Wide Web through SpringerLink Soft computing. Renewable energy sources Data processing. Gopalakrishnan, Kasthurirangan editor. Khaitan, Siddhartha Kumar editor. Kalogirou, Soteris editor. SpringerLink (Online service). http://link.springer.com/book/10.1007/978-3-642-22176-7 Available for University of the Philippines Diliman via SpringerLink. Click here to access FO UPD DMLR Electronic Resource