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
Evolution of competition in energy alternative pathway and the influence of energy policy on economic growth
Honglin Yang,Lin Wang,Lixin Tian +2 more
TL;DR: Wang et al. as discussed by the authors investigated the influence of energy policy on economic growth by using a dynamical system method, and the relation between energy and economic growth was taken into account, and a dynamic evolution model was established.
Journal Article
Design and Control Strategy of Diffused Air Aeration System
TL;DR: A new design of diffused aeration system using fuel cell as a power source and fuzzy logic control Technique (FLC) is used for controlling the speed of air flow rate from the blower to air piping connected to the pond by adjusting blower speed.
Journal ArticleDOI
Research Progress and Perspectives of Solid Fuels Chemical Looping Reaction with Fe-Based Oxygen Carriers
TL;DR: In this article , the general view of Fe-based oxygen carrier including iron ores and artificial synthesis is reviewed, including mechanical mixing, impregnation, spraying drying, freeze granulation, sol-gel, solution combustion, and coprecipitation method.
Journal ArticleDOI
Performance Optimization of a Thermoelectric Device by Using a Shear Thinning Nanofluid and Rotating Cylinder in a Cavity with Ventilation Ports
TL;DR: In this article , the combined effects of using a rotating cylinder and shear thinning nanofluid on the performance improvements of a thermoelectric generator (TEG)-installed cavity with multiple ventilation ports are numerically assessed.
Book ChapterDOI
Neural Networks Applied to Short Term Load Forecasting: A Case Study
TL;DR: ANN models are used with input variables such as apartment area, numbers of occupants, electrical appliance consumption and time, in order to achieve a robust model to be used in forecasting energy consumption of general homes.
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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