TY  - BOOK
AU  - Gopalakrishnan,Kasthurirangan
AU  - Khaitan,Siddhartha Kumar
AU  - Kalogirou,Soteris
ED  - SpringerLink (Online service)
TI  - Soft Computing in Green and Renewable Energy Systems
T2  - Studies in Fuzziness and Soft Computing,
SN  - 9783642221767
AV  - Q342 
U1  - 006.3 23
PY  - 2011///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Renewable energy sources
KW  - Biotechnology
KW  - Computational Intelligence
KW  - Renewable and Green Energy
KW  - Artificial Intelligence (incl. Robotics)
KW  - Environmental Engineering/Biotechnology
N1  - 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
N2  - 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. 
UR  - http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-22176-7
ER  -