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
Artificial intelligence driven hydrogen and battery technologies – A review
A Sai Ramesh,S. T. Vigneshwar,Sundaram Vickram,Sivasubramanian Manikandan,R. Subbaiya,Natchimuthu Karmegam,Woon Yong Kim +6 more
TL;DR: In this paper , a review of state-of-the-art artificial intelligence for hydrogen and battery technology is presented, where the authors demonstrate the contribution of various AI techniques, its algorithms and models in hydrogen energy industry, as well as smart battery manufacturing, and optimization.
Proceedings ArticleDOI
HVAC Fan Mechinery Fault Diagnosis Based on ANN and D-S Evidence Theory
TL;DR: A novel approach to HVAC fan machinery fault diagnosis based on combination of artificial neuron network and D-S evidence theory is presented, where the output values of BPNN are directly taken as the basic probability assignment of the proposition in the frame of discernment.
Proceedings ArticleDOI
Artificial Neural Network based Controller for Energy Management in a Solar Home in Algeria
TL;DR: The results show that the proposed ANN algorithm can reduce the energy consumption of the home without affecting customer comforts.
Journal ArticleDOI
Electrical equivalent model of intermediate band solar cell using PSpice
TL;DR: The artificial neural networks and the analog behavior modeling of PSpice are implemented and used to establish the IBSCS model on PSPICE simulator, and the obtained results can be extended to other solar cells for motivating experimental efforts to realize these promising photovoltaic devices for low cost and high efficiency.
Monthly Temperature Prediction using ANNs and ANFIS (Case Study: Tehran City)
TL;DR: In this article, two intelligent models including ANNs and ANFIS for monthly minimum, maximum, and mean temperatures estimation were developed in synoptic station of Tehran, Iran.
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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