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https://hdl.handle.net/20.500.14279/1453
Τίτλος: | Optimization of solar systems using artificial neural-networks and genetic algorithms | Συγγραφείς: | Kalogirou, Soteris A. | Major Field of Science: | Engineering and Technology | Field Category: | Mechanical Engineering | Λέξεις-κλειδιά: | Industrial-process heat system;Solar systems;Genetic algorithms | Ημερομηνία Έκδοσης: | Απρ-2004 | Πηγή: | Applied Energy, Vol. 77, no. 4, 2004, pp. 383-405 | Volume: | 77 | Issue: | 4 | Start page: | 383 | End page: | 405 | Περιοδικό: | Applied Energy | Περίληψη: | 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: | https://hdl.handle.net/20.500.14279/1453 | ISSN: | 03062619 | DOI: | 10.1016/S0306-2619(03)00153-3 | Rights: | © Elsevier 2003 | Type: | Article | Affiliation: | Higher Technical Institute Cyprus | Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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