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.
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