Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/157
Title: Optimization of solar systems using artificial neural-networks and genetic algorithms
Authors: Kalogirou, Soteris A. 
Kalogirou, Soteris A. 
Keywords: Industrial-process heat system
Solar systems
Genetic algorithms
Issue Date: 2004
Publisher: Elsevier B. V.
Source: Applied Energy, Vol. 77, no. 4, 2004, pp. 383-405
Abstract: The objective of this work is to use artificial intelligence methods, like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modeled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings: thus the design time is reduced substantially. As an example, the optimization of an industrial process heat-system employing flat-plate collectors is presented. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs or by trial-and-error, which in most cases depends on the intuition of the engineer.
URI: http://ktisis.cut.ac.cy/handle/10488/157
ISSN: 0306-2619
DOI: 10.1016/S0306-2619(03)00153-3
Rights: Copyright © 2003 Elsevier Ltd. All rights reserved.
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