Artificial Neural Networks for Renewable Energy Systems and Real World Applications

Artificial Neural Networks for Renewable Energy Systems and Real World Applications

ANN-based methods have attracted the attention of scientists and researchers in different engineering and industrial disciplines, making this book a useful reference for all researchers and engineers interested in artificial networks, ...

Author: Mohamed Elasyed Abd Elaziz

Publisher: Academic Press

ISBN: 9780128231869

Category: Technology & Engineering

Page: 500

View: 422

Artificial Neural Networks for Renewable Energy Systems and Real-World Applications presents current trends for the solution of complex engineering problems in the application, modeling, analysis, and optimization of different energy systems and manufacturing processes. With growing research catering to the applications of neural networks in specific industrial applications, this reference provides a single resource catering to a broader perspective of ANN in renewable energy systems and manufacturing processes. ANN-based methods have attracted the attention of scientists and researchers in different engineering and industrial disciplines, making this book a useful reference for all researchers and engineers interested in artificial networks, renewable energy systems, and manufacturing process analysis. Includes illustrative examples on the design and development of ANNS for renewable and manufacturing applications Features computer-aided simulations presented as algorithms, pseudocodes and flowcharts Covers ANN theory for easy reference in subsequent technology specific sections
Categories: Technology & Engineering

World Renewable Energy Congress VI

World Renewable Energy Congress VI

2163 Editor: A.A.M. Sayigh PERFORMANCE PREDICTION OF A SOLAR WATER HEATER USING ARTIFICIAL NEURAL NETWORKS Soteris A. ... to explore the possibility of using neural network models in real world applications such as in control systems, ...

Author: A. A. M. Sayigh

Publisher: Elsevier

ISBN: 0080540511

Category: Science

Page: 2914

View: 328

The World Renewable Energy Congress is a key event at the start of the 21st century. It is a vital forum for researchers with an interest in helping renewables to reach their full potential. The effects of global warming and pollution are becoming more apparent for all to see - and the development of renewable solutions to these problems is increasingly important globally. If you were unable to attend the conference, the proceedings will provide an invaluable comprehensive summary of the latest topics and papers.
Categories: Science

Artificial Intelligence for Renewable Energy Systems

Artificial Intelligence for Renewable Energy Systems

This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design.

Author: Ajay Kumar Vyas

Publisher: John Wiley & Sons

ISBN: 9781119761693

Category: Computers

Page: 272

View: 818

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
Categories: Computers

Applications of Nature Inspired Computing in Renewable Energy Systems

Applications of Nature Inspired Computing in Renewable Energy Systems

Despite these algorithms having some limitations, the advantages are much more numerous and real-world applications are reaffirming this matter, notably in renewable energy systems, such as fuel cells, photovoltaic cells, wind turbines, ...

Author: Mellal, Mohamed Arezki

Publisher: IGI Global

ISBN: 9781799885634

Category: Technology & Engineering

Page: 326

View: 914

Renewable energy is crucial to preserve the environment. This energy involves various systems that must be optimized and assessed to provide better performance; however, the design and development of renewable energy systems remains a challenge. It is crucial to implement the latest innovative research in the field in order to develop and improve renewable energy systems. Applications of Nature-Inspired Computing in Renewable Energy Systems discusses the latest research on nature-inspired computing approaches applied to the design and development of renewable energy systems and provides new solutions to the renewable energy domain. Covering topics such as microgrids, wind power, and artificial neural networks, it is ideal for engineers, industry professionals, researchers, academicians, practitioners, teachers, and students.
Categories: Technology & Engineering

Soft Computing in Green and Renewable Energy Systems

Soft Computing in Green and Renewable Energy Systems

Soft computing methodologies, of which artificial neural networks (ANNs), genetic algorithms (GAs), ... have gained much attention in recent years as practical tools to analyze complex problems in real-world applications.

Author: Kasthurirangan Gopalakrishnan

Publisher: Springer Science & Business Media

ISBN: 9783642221750

Category: Computers

Page: 306

View: 577

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.
Categories: Computers

Introduction to AI Techniques for Renewable Energy System

Introduction to AI Techniques for Renewable Energy System

Fuzzy algorithm for estimation of solar irradiation from sunshine duration. ... Artificial neural networks in renewable energy systems applications: A review. ... Big IoT data mining for real-time energy disaggregation in buildings.

Author: Suman Lata Tripathi

Publisher: CRC Press

ISBN: 9781000392456

Category: Technology & Engineering

Page: 432

View: 914

Introduction to AI techniques for Renewable Energy System Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems. Features Focuses on a significant area of concern to develop a foundation for the implementation of renewable energy system with intelligent techniques Showcases how researchers working on renewable energy systems can correlate their work with intelligent and machine learning approaches Highlights international standards for intelligent renewable energy systems design, reliability, and maintenance Provides insights on solar cell, biofuels, wind, and other renewable energy systems design and characterization, including the equipment for smart energy systems This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.
Categories: Technology & Engineering

Design Analysis and Applications of Renewable Energy Systems

Design  Analysis and Applications of Renewable Energy Systems

Hence, they are equipped with sensors that log real-time data at the component level. This has consequentially led to increased data veracity and velocity in renewable energy systems. Hence, a paradigm shift from the hard computing ...

Author: Ahmad Taher Azar

Publisher: Academic Press

ISBN: 9780323859912

Category: Science

Page: 760

View: 966

Design, Analysis and Applications of Renewable Energy Systems covers recent advancements in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems as conveyed by leading energy systems engineering researchers. The book focuses on present novel solutions for many problems in the field, covering modeling, control theorems and the optimization techniques that will help solve many scientific issues for researchers. Multidisciplinary applications are also discussed, along with their fundamentals, modeling, analysis, design, realization and experimental results. This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work. Presents some of the latest innovative approaches to renewable energy systems from the point-of-view of dynamic modeling, system analysis, optimization, control and circuit design Focuses on advances related to optimization techniques for renewable energy and forecasting using machine learning methods Includes new circuits and systems, helping researchers solve many nonlinear problems
Categories: Science

Real World Applications of Genetic Algorithms

Real World Applications of Genetic Algorithms

Machine Technique for Power Tracing in Deregulated Power Systems 163 Goldberg, D. E. (1989). Genetic algorithms in search, optimization, ... Reactive power transfer allocation method with the application of artificial neural network.

Author: Olympia Roeva

Publisher: BoD – Books on Demand

ISBN: 9789535101468

Category: Computers

Page: 378

View: 417

The book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using different hybrid intelligent approaches. Multi-objective optimization has been available for about two decades, and its application in real-world problems is continuously increasing. Furthermore, many applications function more effectively using a hybrid systems approach. The book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. The book examines various examples of algorithms in different real-world application domains as graph growing problem, speech synthesis, traveling salesman problem, scheduling problems, antenna design, genes design, modeling of chemical and biochemical processes etc.
Categories: Computers

Artificial Intelligence in Energy and Renewable Energy Systems

Artificial Intelligence in Energy and Renewable Energy Systems

Such an appreciation is very important because , when used in specific applications , the artificial data should produce comparable results to those the real time series yield . Artificial Neural Networks ( ANNs ) have long been widely ...

Author: Soteris Kalogirou

Publisher: Nova Publishers

ISBN: 1600212611

Category: Science

Page: 471

View: 250

This book presents state of the art applications of artificial intelligence in energy and renewable energy systems design and modelling. It covers such topics as solar energy, wind energy, biomass and hydrogen as well as building services systems, power generation systems, combustion processes and refrigeration. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities dealing with modelling and performance prediction of energy and renewable energy systems.
Categories: Science

Intelligent Paradigms for Smart Grid and Renewable Energy Systems

Intelligent Paradigms for Smart Grid and Renewable Energy Systems

This book series publishes research on the analysis and development of algorithms for intelligent systems with their applications to various real world problems. It covers research related to autonomous agents, multi-agent systems, ...

Author: B. Vinoth Kumar

Publisher: Springer Nature

ISBN: 9789811599682

Category: Technology & Engineering

Page: 390

View: 458

This book addresses and disseminates state-of-the-art research and development in the applications of intelligent techniques for smart grids and renewable energy systems. This helps the readers to grasp the extensive point of view and the essence of the recent advances in this field. The book solicits contributions from active researchers which include theory, case studies and intelligent paradigms pertaining to the smart grid and renewable energy systems. The prospective audience would be researchers, professionals, practitioners and students from academia and industry who work in this field.
Categories: Technology & Engineering