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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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.

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

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

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

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Ammar Hamed Elsheikh

Mohamed Abd Elaziz

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