
Artificial Neural Networks for Renewable Energy Systems and Real-World Applications
- 1st Edition - September 8, 2022
- Imprint: Academic Press
- Editors: Ammar Hamed Elsheikh, Mohamed Abd Elaziz
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 7 9 3 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 1 8 6 - 9
Artificial Neural Networks for Renewable Energy Systems and Real-World Applications presents current trends for the solution of complex engineering problems in the applicati… Read more

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Request a sales quoteArtificial 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
Researchers in modelling, analysis, and economic evaluation for engineering systems
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- About the editors
- Chapter one. Basics of artificial neural networks
- Abstract
- Table of Contents
- 1.1 Artificial neural networks
- 1.2 Types of neural networks
- 1.3 Conclusion
- References
- Chapter two. Artificial neural network applied to the renewable energy system performance
- Abstract
- Table of Contents
- Nomenclature
- 2.1 Introduction
- 2.2 Description of experimental equipment
- 2.3 Development of the neural network model
- 2.4 Neural network model
- 2.5 Conclusions
- References
- Chapter three. Applications of artificial neural networks in concentrating solar power systems
- Abstract
- Table of Contents
- 3.1 Introduction
- 3.2 Concentrating solar collectors
- 3.3 Artificial neural networks
- 3.4 Artificial neural network applications in concentrating solar power systems
- 3.5 Prospective and challenges
- 3.6 Conclusions and future recommendations
- References
- Chapter four. Neural simulation of a solar thermal system in low temperature
- Abstract
- Table of Contents
- 4.1 Introduction
- 4.2 Materials and methods
- 4.3 Results
- 4.4 Discussion
- 4.5 Conclusions
- Acknowledgments
- References
- Chapter five. Solar energy modelling and forecasting using artificial neural networks: a review, a case study, and applications
- Abstract
- Table of Contents
- 5.1 Introduction
- 5.2 Solar radiation modeling
- 5.3 Used data and statistical analysis
- 5.4 Results and discussions
- 5.5 Solar energy conversion systems: an overview
- 5.6 Conclusions
- Appendix
- References
- Chapter six. Digital twin predictive maintenance strategy based on machine learning improving facility management in built environment
- Abstract
- Table of Contents
- 6.1 Introduction
- 6.2 Case study
- 6.3 Proposed predictive maintenance strategy
- 6.4 Results and discussions
- 6.5 Conclusions
- References
- Chapter seven. Artificial neural network and desalination systems
- Abstract
- Table of Contents
- 7.1 Introduction
- 7.2 Methods of desalination
- 7.3 Economics related to desalination
- 7.4 Future expectance
- 7.5 Solar still
- 7.6 Types of solar still
- 7.7 Artificial neural network as a prediction method for the performance of desalination systems
- 7.8 Conclusions
- References
- Chapter eight. Artificial neural networks for engineering applications: a review
- Abstract
- Table of Contents
- 8.1 Introduction
- 8.2 Application of artificial neural networks in engineering fields
- 8.3 Conclusion
- Conflicts of interest
- References
- Chapter nine. Incremental deep learning model for plant leaf diseases detection
- Abstract
- Table of Contents
- 9.1 Introduction
- 9.2 Related works
- 9.3 Proposed approach
- 9.4 Experimental results
- 9.5 Conclusion
- References
- Chapter ten. Incremental learning of convolutional neural networks in bioinformatics
- Abstract
- Table of Contents
- 10.1 Introduction
- 10.2 Incremental learning of convolutional neural networks
- 10.3 Incremental learning of convolutional neural networks in bioinformatics
- 10.4 Discussion
- 10.5 Conclusion
- References
- Chapter eleven. Hybrid Arabic classification techniques based on naïve Bayes algorithm for multidisciplinary applications
- Abstract
- Table of Contents
- 11.1 Introduction
- 11.2 Related works
- 11.3 The proposed method
- 11.4 Results and discussion
- 11.5 Conclusion and future work
- References
- Index
- Edition: 1
- Published: September 8, 2022
- Imprint: Academic Press
- No. of pages: 288
- Language: English
- Paperback ISBN: 9780128207932
- eBook ISBN: 9780128231869
AE
Ammar Hamed Elsheikh
MA