Elsheikh A.H., Elaziz M.E.A. (eds.) Artificial Neural Networks for Renewable Energy Systems and Real-World Applications

Academic Press; Elsevier, 2022. — 290 p. — ISBN: 978-0-12-820793-2.Artificial Neural Networks for Renewable Energy Systems and Real-World Applications present 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.Artificial neural networks (ANNs) are widely distributed processors made up of basic processing units called neurons. They have a built-in capability for storing experimental knowledge that is suitable for use. High-speed information processing, routing capabilities, fault tolerance, adaptiveness, generalization, and robustness are all excellent characteristics of ANNs. These features make ANNs useful tools for modeling, optimizing, and predicting the performance of various engineering systems. As a result, they have been used to solve complex nonlinear engineering problems in several real-world applications with acceptable cost and efficient computing time. In this section, we describe four ANN models, including the multilayer perceptron (MLP), wavelet neural network (WNN), radial basis function (RBF), and Elman neural network (ENN).Includes illustrative examples of the design and development of ANNS for renewable and manufacturing applications.
Features computer-aided simulations presented as algorithms, pseudocode, and flowcharts.
Covers ANN theory for easy reference in subsequent technology-specific sections.Basics of artificial neural networks.
Artificial neural network applied to the renewable energy system performance.
Applications of artificial neural networks in concentrating solar power systems.
Neural simulation of a solar thermal system in low temperature.
Solar energy modeling and forecasting using artificial neural networks: a review, a case study, and applications.
Digital twin predictive maintenance strategy based on machine learning improving facility management in the built environment.
Artificial neural network and desalination systems.
Artificial neural networks for engineering applications: a review.
The incremental deep learning model for plant leaf disease detection.
Incremental learning of convolutional neural networks in bioinformatics.
Hybrid Arabic classification techniques based on naïve Bayes algorithm for multidisciplinary applications.