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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.
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 being used to solve complex nonlinear engineering problems in a number of 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 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
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 modelling 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 built environment
Artificial neural network and desalination systems
Artificial neural networks for engineering applications: a review
Incremental deep learning model for plant leaf diseases detection
Incremental learning of convolutional neural networks in bioinformatics
Hybrid Arabic classification techniques based on naïve Bayes algorithm for multidisciplinary applications