Please use this identifier to cite or link to this item:
Title: Applications of artificial neural networks in energy systems--A review
Authors: Kalogirou, Soteris A. 
Keywords: Artificial Neural Networks (ANN);System modelling;System performance prediction
Category: Environmental Engineering
Field: Engineering and Technology
Issue Date: Jul-1999
Source: Energy Conversion and Management,1999, vol. 40, no. 10, , pp. 1073-1087
Journal: Energy Conversion and Management 
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 in 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 the heat-up response of a solar steam-generating plant, for the estimation of a parabolic trough collector intercept factor, for the estimation of a parabolic trough collector local concentration ratio and for the design of a solar steam generation system. They have also been used for the estimation of heating loads of buildings. 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 energy production and use. The work of other researchers in the field of energy is also reported. This includes the use of artificial neural networks in heating, ventilating and air-conditioning systems, solar radiation, modelling and control of power generation systems, load forecasting and prediction, and refrigeration.
ISSN: 0196-8904
DOI: 10.1016/S0196-8904(99)00012-6
Collaboration : Higher Technical Institute Cyprus
Rights: © Elsevier
Type: Article
Appears in Collections:Άρθρα/Articles

CORE Recommender
Show full item record


checked on Feb 8, 2018


checked on May 27, 2020

Page view(s)

Last Week
Last month
checked on May 23, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.