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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.
Handbook of Artificial Intelligence Techniques in Photovoltaic
Systems: Modelling, Control, Optimization, Forecasting and Fault
Diagnosis provides readers with a comprehensive and detailed
overview of the role of artificial intelligence in PV systems.
Covering up-to-date research and methods on how, when and why to
use and apply AI techniques in solving most photovoltaic problems,
this book will serve as a complete reference in applying
intelligent techniques and algorithms to increase PV system
efficiency. Sections cover problem-solving data for challenges,
including optimization, advanced control, output power forecasting,
fault detection identification and localization, and more.
Supported by the use of MATLAB and Simulink examples, this
comprehensive illustration of AI-techniques and their applications
in photovoltaic systems will provide valuable guidance for
scientists and researchers working in this area.
Thermal Solar Desalination: Methods and Systems presents numerous
thermal seawater desalination technologies varying from the very
simple, easy to construct and operate solar stills, to the more
advance membrane and indirect distillation methods. All types of
solar thermal desalination technologies are presented in detail to
enable readers to comprehend the subject, from design details to
enabling further research to be carried out in this area. The
various units used in desalination are outlined, along with
diagrams of all detailed working principles of desalination methods
and systems. The authors consider the economic aspects of these
processes, demonstrating successful implementation of desalination
units suitable for areas where supplies of fresh water in natural
ways is limited or non-existent.
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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