DESIGN AND CONSTRUCTION OF AN ARTIFICIAL INTELLIGENT SOLAR POWER GENERATION
The objective of this work is to use artificial intelligence methods for the optimal design of solar energy systems. The lifecycle savings of the system is used as the optimization parameter. The variable parameters in this optimization are the collector area, slope and mass flow rate and the volume of the storage tank. An artificial neural network is trained, using the results of a small number of simulations carried out with TRNSYS program, to learn the correlation of the above variable parameters on the auxiliary energy required by the system from which the lifecycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of the variable parameters, which maximizes lifecycle savings. As an example, the optimization of a large hot water system is presented.
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