Soft Computing in Green and Renewable Energy Systems
Gopalakrishnan, Kasthurirangan.
Soft Computing in Green and Renewable Energy Systems [recurso electrónico] / edited by Kasthurirangan Gopalakrishnan, Siddhartha Kumar Khaitan, Soteris Kalogirou. - XIV, 306p. 147 illus., 73 illus. in color. online resource. - Studies in Fuzziness and Soft Computing, 269 1434-9922 ; . - Studies in Fuzziness and Soft Computing, 269 .
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.
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.
9783642221767
Engineering.
Artificial intelligence.
Renewable energy sources.
Biotechnology.
Engineering.
Computational Intelligence.
Renewable and Green Energy.
Artificial Intelligence (incl. Robotics).
Environmental Engineering/Biotechnology.
Q342
006.3
Soft Computing in Green and Renewable Energy Systems [recurso electrónico] / edited by Kasthurirangan Gopalakrishnan, Siddhartha Kumar Khaitan, Soteris Kalogirou. - XIV, 306p. 147 illus., 73 illus. in color. online resource. - Studies in Fuzziness and Soft Computing, 269 1434-9922 ; . - Studies in Fuzziness and Soft Computing, 269 .
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.
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.
9783642221767
Engineering.
Artificial intelligence.
Renewable energy sources.
Biotechnology.
Engineering.
Computational Intelligence.
Renewable and Green Energy.
Artificial Intelligence (incl. Robotics).
Environmental Engineering/Biotechnology.
Q342
006.3
