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Artificial Neural Networks for Renewable Energy Systems and Real-World Applications

  • ID: 5146425
  • Book
  • June 2021
  • 500 Pages
  • Elsevier Science and Technology
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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
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Part I: ANN fundamentals 1. Introduction 2. Basic Principles of ANNs 3. Types of ANNs

Part II: Applications of ANNs in Renewable Energy Systems 4. Applications of ANN in Solar Collectors 5. Applications of ANN in Solar Water Desalination 6. Modeling of Solar Cells Using of ANN 7. Applications of ANN in Wind Energy 8. Applications of ANN in Biofuel

Part III: Applications of ANNs in Manufacturing Processes 9. Applications of ANN in Machining 10. Applications of ANN in Metal forming 11. Applications of ANN in Welding 12. Applications of ANN in Industrial Robots

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Elsheikh, Ammar Hamed
Ammar Elsheikh received B.S. and M.S. degrees in mechanical engineering from Tanta University, Tanta, Egypt and Ph.D. degree from Huazhong University of Science and Technology, Wuhan, China. He is currently working as an associative professor in both universities. His research interests include renewable energy, manufacturing systems, and the applications of ANN and metaheuristic techniques in engineering problems.
Abd Elaziz, Mohamed
Mohamed Abd Elaziz received B.S., M.S., and PhD degrees in Computer science from the Zagazig University, Zagazig, Eypt. He is currently working as an associative professor in Wuhan University of Technology, Wuhan, China. He is the author of more than 50 articles. His research interests include machine learning, signal processing, image processing, and metaheuristic techniques.
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