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Format: 1 online resource (290 pages)

ISBN: 0-12-823186-6

Content: 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. --

Note: Front Cover -- Artificial Neural Networks for Renewable Energy Systems and Real-world Applications -- Copyright Page -- Contents -- List of contributors -- About the editors -- 1. Basics of artificial neural networks -- 1.1 Artificial neural networks -- 1.2 Types of neural networks -- 1.2.1 Multilayer perceptron neural network -- 1.2.2 Wavelet neural networks -- 1.2.3 Radial basis function -- 1.2.4 Elman neural network -- 1.2.5 Statistical performance evaluation criteria -- 1.3 Conclusion -- References -- 2. Artificial neural network applied to the renewable energy system performance -- 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 -- 3. Applications of artificial neural networks in concentrating solar power systems -- 3.1 Introduction -- 3.2 Concentrating solar collectors -- 3.2.1 Parabolic trough collector -- 3.2.2 Solar dish collector -- 3.2.3 Linear Fresnel reflector -- 3.2.4 Central tower receiver -- 3.3 Artificial neural networks -- 3.3.1 Conceptual structure of artificial neural networks -- 3.3.2 Performance evaluation criteria of the artificial neural network model -- 3.4 Artificial neural network applications in concentrating solar power systems -- 3.5 Prospective and challenges -- 3.6 Conclusions and future recommendations -- References -- 4. Neural simulation of a solar thermal system in low temperature -- 4.1 Introduction -- 4.2 Materials and methods -- 4.2.1 Meteorological data -- 4.2.2 Solar thermal system -- 4.2.3 Artificial neural networks of the components of the solar thermal system -- 4.2.4 Neural simulation of the solar thermal system -- 4.3 Results -- 4.3.1 Neural simulation during a day -- 4.3.1.1 Neural simulation during the day 10/10/2011. , 4.3.1.2 Neural simulation during the day 11/28/2011 -- 4.3.2 Neural simulation during 2012 -- 4.3.3 Neural simulation for 10 years -- 4.3.4 f-Chart method -- 4.3.5 Neural simulation versus f-chart method -- 4.4 Discussion -- 4.5 Conclusions -- Acknowledgments -- References -- 5. Solar energy modelling and forecasting using artificial neural networks: a review, a case study, and applications -- 5.1 Introduction -- 5.2 Solar radiation modeling -- 5.2.1 Solar constant and extraterrestrial radiation -- 5.2.2 Instantaneous and hourly solar radiation models -- 5.2.3 Modeling of daily global solar radiation (DGSR) and monthly average global solar radiation (MAGSR) based on ANN techn... -- 5.3 Used data and statistical analysis -- 5.3.1 Local weather information -- 5.3.2 Statistical analysis -- 5.4 Results and discussions -- 5.4.1 Best combinations of inputs in modeling and forecasting of DGSR -- 5.4.2 Best combinations of inputs in modeling and forecasting of MAGSR -- 5.5 Solar energy conversion systems: an overview -- 5.6 Conclusions -- Appendix -- References -- 6. Digital twin predictive maintenance strategy based on machine learning improving facility management in built environment -- 6.1 Introduction -- 6.2 Case study -- 6.2.1 Maintenance system -- 6.3 Proposed predictive maintenance strategy -- 6.4 Results and discussions -- 6.4.1 Digital twin for building management systems -- 6.5 Conclusions -- References -- 7. Artificial neural network and desalination systems -- 7.1 Introduction -- 7.2 Methods of desalination -- 7.2.1 Multistage flash distillation -- 7.2.2 Multiple-effect distillation -- 7.2.3 Vapor compression distillation -- 7.2.4 Reverse osmosis -- 7.2.5 Freezing -- 7.2.6 Solar distillation -- 7.2.7 Potabilization -- 7.3 Economics related to desalination -- 7.4 Future expectance -- 7.5 Solar still -- 7.6 Types of solar still. , 7.6.1 Single-effect solar still -- 7.6.1.1 Active still -- 7.6.1.2 Passive still -- 7.6.2 Multieffect solar still -- 7.6.2.1 Active still -- 7.6.2.2 Passive still -- 7.7 Artificial neural network as a prediction method for the performance of desalination systems -- 7.7.1 Network architecture -- 7.7.2 Application of artificial neural networks in desalination systems -- 7.8 Conclusions -- References -- 8. Artificial neural networks for engineering applications: a review -- 8.1 Introduction -- 8.2 Application of artificial neural networks in engineering fields -- 8.2.1 Chemical engineering -- 8.2.2 Civil engineering -- 8.2.3 Computer engineering -- 8.2.4 Power and energy engineering -- 8.2.5 Construction engineering -- 8.2.6 Mechanical engineering -- 8.2.7 Geotechnical engineering -- 8.3 Conclusion -- Conflicts of interest -- References -- 9. Incremental deep learning model for plant leaf diseases detection -- 9.1 Introduction -- 9.2 Related works -- 9.3 Proposed approach -- 9.3.1 The deep learning model -- 9.3.2 DataSelector and Memory -- 9.3.2.1 Feature-extractor -- 9.3.2.2 Principal component analysis -- 9.3.2.3 Clustering -- 9.3.2.4 K-neighbors -- 9.3.2.5 Selected data -- 9.3.2.6 Memory -- 9.4 Experimental results -- 9.4.1 Plant diseases dataset -- 9.4.2 The model's architecture -- 9.4.3 Hyperparameters and evaluation -- 9.4.4 Influence of memory size and batches -- 9.4.5 Comparison with iCaRL -- 9.5 Conclusion -- References -- 10. Incremental learning of convolutional neural networks in bioinformatics -- 10.1 Introduction -- 10.1.1 Replay-based methods -- 10.1.2 Regularization-based methods -- 10.1.3 Parameter isolation-based methods -- 10.2 Incremental learning of convolutional neural networks -- 10.2.1 iCaRL: incremental classifier and representation learning -- 10.2.2 LwF: learning without forgetting. , 10.2.3 Tree-CNN: tree convolutional neural networks -- 10.3 Incremental learning of convolutional neural networks in bioinformatics -- 10.3.1 SupportNet: a novel incremental learning framework through deep learning and support data -- 10.3.2 Continual class incremental learning for computerized tomography (CT) thoracic segmentation -- 10.4 Discussion -- 10.5 Conclusion -- References -- 11. Hybrid Arabic classification techniques based on naïve Bayes algorithm for multidisciplinary applications -- 11.1 Introduction -- 11.2 Related works -- 11.2.1 Data mining -- 11.2.2 Text classification -- 11.2.2.1 Information retrieval using machine learning -- 11.2.2.2 Machine learning for text classification -- 11.2.3 Previous studies -- 11.3 The proposed method -- 11.3.1 Collecting information -- 11.3.2 Obtaining datasets -- 11.3.2.1 Open Source Arabic Corpus -- 11.3.2.2 BBC Arabic News -- 11.3.2.3 CNN Arabic News -- 11.3.3 Preprocessing -- 11.3.3.1 Tokenization -- 11.3.3.2 P stemmer -- 11.3.3.3 Term frequency-inverse document frequency -- 11.3.4 The classification process -- 11.3.4.1 WEKA data mining tool -- 11.3.4.2 Classifying algorithms -- 11.3.4.2.1 Naïve Bayes algorithm -- 11.3.4.2.2 New NBJ48 -- 11.3.4.2.3 Support vector machine -- 11.3.4.2.4 Artificial neural networks -- 11.3.4.2.5 J48 -- 11.3.5 Evaluation -- 11.4 Results and discussion -- 11.4.1 Results -- 11.5 Conclusion and future work -- References -- Index -- Back Cover.

Additional Edition: Print version: Elsheikh, Ammar Hamed Artificial Neural Networks for Renewable Energy Systems and Real-World Applications San Diego : Elsevier Science & Technology,c2022 ISBN 9780128207932

Language: English