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
Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems
Chokri Ben Salah,Mohamed Ouali +1 more
TL;DR: In this article, the authors proposed two methods of maximum power point tracking using a fuzzy logic and a neural network controller for photovoltaic systems, which are validated on a 100 Wp PVP (two parallels SM50-H panel) connected to a 24 V dc load.
Journal ArticleDOI
The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria
TL;DR: In this article, the profile of wind speed in Nigeria is modelled using artificial neural network (ANN), which consists of 3-layered, feed-forward, back-propagation network with different configurations using the Neural Toolbox for MATLAB.
Journal ArticleDOI
Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
TL;DR: An original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP) and the multi-layer perceptron (MLP) is proposed.
Journal ArticleDOI
Review: Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling
TL;DR: A systematic protocol for the development and documentation of ANN models is introduced and shows that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.
Journal ArticleDOI
Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems
TL;DR: In this paper, a fuzzy multi-criteria method (TOPSIS fuzzy) was proposed to compare different heat transfer fluids (HTF) in order to investigate the feasibility of utilizing a molten salt.
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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