Journal ArticleDOI
Artificial neural networks in renewable energy systems applications: a review
Reads0
Chats0
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
More filters
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
More filters
Journal ArticleDOI
Thermosiphon solar domestic water heating systems: long-term performance prediction using artificial neural networks
TL;DR: In this article, the authors used artificial neural networks (ANN) for the long-term performance prediction of thermosiphonic type solar domestic water heating (SDWH) systems.
Proceedings ArticleDOI
Application of recurrent neural network for short term load forecasting in electric power system
TL;DR: Different network architectures from fully recurrent (complete connectivity) to no feedback paths (only feedforward paths) are modelled and their characteristics for short term load forecasting are compared.
Proceedings ArticleDOI
Artificial neural networks application for current rating of overhead lines
M. Negnevitsky,Tan Loc Le +1 more
TL;DR: The developed intelligent system can be used to assist operators in loading of power transmission lines in different operating, ambient, cloud and ground reflection conditions and assist the operators to determine the permissible duration of the conductor overload.
Proceedings ArticleDOI
Improving recurrent network load forecasting
TL;DR: The use of a continuous learning scheme and a robust learning scheme, which appeared to be necessary when using a MA part, enables us to reach a good precision of the forecast, compared to the accuracy of the model in use at the utility at present.
Proceedings ArticleDOI
An adaptive and modular recurrent neural network based power system load forecaster
TL;DR: A recurrent neural network based hourly load forecaster for hourly prediction of power system loads, consisting of 24 RNNs, one for each hour of the day, trained with Pineda's recurrent backpropagation algorithm.
Related Papers (5)
Artificial intelligence techniques for photovoltaic applications: A review
Adel Mellit,Soteris A. Kalogirou +1 more