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
Performance evaluation of environmentally benign nonionic biosurfactant for enhanced oil recovery
Seokju Seo,Mohammad Mastiani,Babak Mosavati,Derek Michael Peters,Philippe Mandin,Myeongsub Kim +5 more
TL;DR: In this paper, the performance of a highly biodegradable nonionic surfactant derived from tannic acid, a possible alternative, was evaluated using a microfluidic technology for the replacement of chemically synthesis surfactants by green chemistry products.
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Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks
TL;DR: This study proposes a novel approach of using genetically optimized non-linear auto-regressive recurrent neural networks (NARX) for ultra-short-term forecasting of PV power output, and found that the proposed models offer very good estimates of output power.
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100 Years of daylighting: A chronological review of daylight prediction and calculation methods
TL;DR: A comprehensive review of over 100 years of growing fundamental directions to predict the amount of daylight inside buildings, with a particular focus on tracing sky models, weather datasets, building geometry and daylight calculation methods, is provided.
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A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity
TL;DR: In this article, a flexible neuro-fuzzy approach for location optimization of solar plants with possible complexity and uncertainty is presented, which is composed of artificial neural network (ANN) and fuzzy data envelopment analysis (FDEA).
Journal ArticleDOI
Prediction of Thermal Performance of Unidirectional Flow Porous Bed Solar Air Heater with Optimal Training Function Using Artificial Neural Network
TL;DR: In this article, Artificial Neural Network (ANN) has been used to predict the thermal performance of unidirectional flow porous bed solar air heater, which was structured on the basis of data sets obtained from experiments and values of thermal efficiency of solar AHE.
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
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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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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