abstract bibtex

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.

@article{ Kalogirou2004, 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.}, author = {Kalogirou, Soteris A.}, issn = {0306-2619}, journal = {Applied Energy}, month = {April}, number = {4}, pages = {383--405}, title = {{Optimization of solar systems using artificial neural-networks and genetic algorithms}}, volume = {77}, year = {2004} }

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