Genetic algorithms were used by the author in a number of optimization problems: the optimal design of flat-plate solar collectors (Kalogirou, 2003c), predicting the optimal sizing coefficient of photovoltaic supply systems (Mellit and Kalogirou, 2006a), and the optimum selection of the fenestration openings in buildings (Kalogirou, 2007). They have also been used to optimize solar energy systems, in combination with TRNSYS and ANNs (Kalogirou, 2004a). In this, the system is modeled using the TRNSYS computer program and the climatic conditions of Cyprus. An artificial neural network was 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 was 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 (Kalogirou, 2004a). 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.
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