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
Representation and Prediction of Molecular Diffusivity of Nonelectrolyte Organic Compounds in Water at Infinite Dilution Using the Artificial Neural Network-Group Contribution Method
TL;DR: In this paper, an ANN-GC method is applied to represent and predict the molecular diffusivity of nonelectrolyte organic compounds in water at infinite dilution and 298.15 K. The results show the squared correlation coefficient of 0.996, root-mean-square error of about 0.02, and average absolute deviation lower than 1.5 % for the calculated or predicted property from existing experimental values.
Journal ArticleDOI
Comparison between an artificial neural network and logistic regression in predicting acute graft-vs-host disease after unrelated donor hematopoietic stem cell transplantation in thalassemia patients.
Giovanni Caocci,Roberto Baccoli,Adriana Vacca,Angela Mastronuzzi,Alice Bertaina,Eugenia Piras,Roberto Littera,Franco Locatelli,Carlo Carcassi,Giorgio La Nasa +9 more
TL;DR: Although ANNs are unable to calculate the weight of single variables on outcomes, they were found to have a better performance than LR and could be more efficient in predicting outcomes.
Journal Article
Energy Consumption and Modeling of output energy with Multilayer Feed-Forward Neural Network for Corn Silage in Iran
TL;DR: In this paper, various Artificial Neural Networks (ANNs) were developed to estimate the output energy for corn silage production in Esfahan province, Iran and the results of ANNs analyze showed that the ANN approach appears to be a suitable method for modeling output energy, fuel consumption, CO 2 emission, yield, and energy consumption based on social and technical parameters.
Journal Article
The use of artificial neural networks for forecasting the monthly mean soil temperatures in Adana, Turkey
TL;DR: In this paper, the authors developed an artificial neural network (ANN) model to predict monthly mean soil temperature for the present month by using various previous monthly mean meteorological variables such as the measured soil temperature and other meteorological data between the years of 2000 and 2007 at Adana meteorological station.
Journal ArticleDOI
Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications
TL;DR: In this article, a new method based on artificial neural networks was introduced to spectrally correct the direct normal irradiance for the electrical characterization of an HCPV module. But the method only takes into account the main atmospheric parameters that influence the performance of a high concentrator photovoltaic module: air mass, aerosol optical depth and precipitable water.
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.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Book
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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