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
Solar thermal collectors and applications
TL;DR: A survey of the various types of solar thermal collectors and applications is presented in this paper, where an analysis of the environmental problems related to the use of conventional sources of energy is presented and the benefits offered by renewable energy systems are outlined.
Journal ArticleDOI
State-of-the-art in artificial neural network applications: A survey
Oludare Isaac Abiodun,Oludare Isaac Abiodun,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Nachaat AbdElatif Mohamed,Humaira Arshad +6 more
TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.
Book
Biomass Gasification and Pyrolysis: Practical Design and Theory
TL;DR: In this article, the authors provide a comprehensive coverage of the design, analysis, and operational aspects of biomass gasification, the key technology enabling the production of biofuels from all viable sources.
Journal ArticleDOI
Machine learning methods for solar radiation forecasting: A review
Cyril Voyant,Gilles Notton,Soteris A. Kalogirou,Marie Laure Nivet,Christophe Paoli,Christophe Paoli,Fabrice Motte,Alexis Fouilloy +7 more
TL;DR: An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.
Journal ArticleDOI
Deep Eutectic Solvents: A Review of Fundamentals and Applications.
Benworth Hansen,Stephanie Spittle,Brian Chen,Derrick Poe,Yong Zhang,Jeffrey M. Klein,Alexandre Horton,Laxmi Adhikari,Tamar Zelovich,Brian W. Doherty,Burcu Gurkan,Edward J. Maginn,Arthur J. Ragauskas,Mark Dadmun,Thomas A. Zawodzinski,Gary A. Baker,Mark E. Tuckerman,Robert F. Savinell,Joshua Sangoro +18 more
TL;DR: A detailed review of the current literature reveals the lack of predictive understanding of the microscopic mechanisms that govern the structure-property relationships in deep eutectic solvents, and highlights recent research efforts to elucidate the next steps needed to develop a fundamental framework needed for a deeper understanding.
References
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Journal ArticleDOI
Modeling of solar domestic water heating systems using artificial neural networks
TL;DR: Results indicate that the proposed method can successfully be used for the estimation of the useful energy extracted from the system and the temperature rise in the stored water of solar domestic water heating (SDHW) systems with the minimum of input data.
Journal ArticleDOI
Artificial neural networks used for the performance prediction of a thermosiphon solar water heater
TL;DR: In this article, an artificial neural network (ANN) was used to predict the performance of a thermosiphon solar domestic water heating system, which is measured in terms of the useful energy extracted and the stored water temperature rise.
Journal ArticleDOI
Daily insolation forecasting using a multi-stage neural network
TL;DR: In this paper, a multi-stage neural network is developed for further reduction of the mean error of the forecast insolation by the single-stage Neural Network (NN) for the next day.
Journal ArticleDOI
Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks
TL;DR: In this article, the authors used Artificial Neural Networks (ANNs) for the long-term performance prediction of forced circulation type solar domestic water heating (SDWH) systems, where two different networks were used due to the different natures of the output required in each case.
Journal ArticleDOI
ANN based peak power tracking for PV supplied DC motors
Mummadi Veerachary,Narri Yadaiah +1 more
TL;DR: A gradient descent algorithm is used to train the ANN controller for the identification of the maximum power point of the Solar Cell Array (SCA) and gross mechanical energy operation of the combined system.
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