Journal ArticleDOI
Artificial neural networks in renewable energy systems applications: a review
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TLDR
In this article, the authors present various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use.Abstract:
Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling such as in implementing complex mappings and system identification. This paper presents various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic trough collector intercept factor and local concentration ratio and for the modelling and performance prediction of solar water heating systems. They have also been used for the estimation of heating loads of buildings, for the prediction of air flow in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all those models a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use. The work of other researchers in the field of renewable energy and other energy systems is also reported. This includes the use of artificial neural networks in solar radiation and wind speed prediction, photovoltaic systems, building services systems and load forecasting and prediction.read more
Citations
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Journal ArticleDOI
An advanced ANN-based method to estimate hourly solar radiation from multi-spectral MSG imagery
S. Quesada-Ruiz,Alvaro Linares-Rodriguez,José A. Ruiz-Arias,David Pozo-Vázquez,J. Tovar-Pescador +4 more
TL;DR: In this paper, a new method to derive hourly global horizontal irradiance (GHI) estimates from Meteosat Second Generation (MSG) imagery is presented based on an optimized Artificial Neural Network (ANN) ensemble model using a selection of the best ANN models identified from an initial ensemble that discerns between different sky conditions and an additional ensemble that considers all sky conditions together.
Journal ArticleDOI
Machine Learning for the prediction of the dynamic behavior of a small scale ORC system
TL;DR: The Long Short Term Memory architecture resulted as the highest performing, in that it correctly predicts the dynamics of the system, showing an error prediction lower than 5% and 10% respectively for what concern the prediction 10 and 60 seconds ahead.
Journal ArticleDOI
Application of artificial neural network for the prediction of jaggery mass during drying inside the natural convection greenhouse dryer
Om Prakash,Anil Kumar +1 more
TL;DR: In this article, an attempt is made to predict the hourly mass of jaggery during the process of drying inside greenhouse dryer under the natural convection mode using Artificial Neural Network (ANN).
Journal ArticleDOI
Artificial Neural Networks and Deep Learning in the Visual Arts: a review
TL;DR: In this article, the authors performed an exhaustive analysis of the use of Artificial Neural Networks and Deep Learning in the Visual Arts. But they focused on the contributions of photography and pictorial art, and there are also other uses for 3D modeling, including video games, architecture and comics.
Journal ArticleDOI
Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration
Soteris A. Kalogirou,Georgios A. Florides,Panayiotis Pouloupatis,Ioannis Panayides,Josephina Joseph-Stylianou,Z. Zomeni +5 more
TL;DR: In this paper a neural network is used for the generation of geothermal maps (contours) of temperature at three depths (20, 50 and 100 m) in Cyprus and a multiple hidden layer feedforward architecture was chosen after testing a number of architectures.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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Learning internal representations by error propagation
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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
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Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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