000			03692nam a22005055i 4500
001			u376434
003			SIRSI
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007			cr nn 008mamaa
008			110809s2011 gw | s |||| 0|eng d
020			_a9783642221767 _9978-3-642-22176-7
040			_cMX-MeUAM
050		4	_aQ342
082	0	4	_a006.3 _223
100	1		_aGopalakrishnan, Kasthurirangan. _eeditor.
245	1	0	_aSoft Computing in Green and Renewable Energy Systems _h[recurso electrónico] / _cedited by Kasthurirangan Gopalakrishnan, Siddhartha Kumar Khaitan, Soteris Kalogirou.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXIV, 306p. 147 illus., 73 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v269
505	0		_aFrom 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.
520			_aSoft 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.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aRenewable energy sources.
650		0	_aBiotechnology.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aRenewable and Green Energy.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aEnvironmental Engineering/Biotechnology.
700	1		_aKhaitan, Siddhartha Kumar. _eeditor.
700	1		_aKalogirou, Soteris. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642221750
830		0	_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v269
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-22176-7
596			_a19
942			_cLIBRO_ELEC
999			_c204314 _d204314