Authors: Soteris A. Kalogirou
Addresses: Higher Technical Institute, PO Box 20423, Nicosia 2152, Cyprus
Abstract: 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 optimisation parameter. The variable parameters in this optimisation 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 maximises lifecycle savings. As an example, the optimisation of a large hot water system is presented. The optimum solution obtained from the present methodology is achieved very quickly as compared to the time required to obtain the same solution by the traditional trial and error method, which would require thousands of runs of TRNSYS to cover all possible combinations considered by the genetic algorithm.
Keywords: artificial neural networks; genetic algorithms; optimisation; solar systems; solar energy; solar power; artificial intelligence; optimal design; lifecycle savings; collector area; slope; mass flow rate; storage tank volume; simulation; hot water systems.
International Journal of Computer Applications in Technology, 2005 Vol.22 No.2/3, pp.90 - 103
Published online: 26 Apr 2005 *Full-text access for editors Access for subscribers Purchase this article Comment on this article