Artificial Neural Networks and Genetic Algorithms for the Optimisation of Solar Thermal Systems
Date Issued
August 2006
Author(s)
Abstract
This paper presents a new method to optimise solar energy systems in order to maximise their
economic benefits. The system is modelled with TRNSYS computer program. An artificial neural
network is trained using a small number of annual TRNSYS simulation results, to learn the
correlation of collector area and storage tank size on the auxiliary energy required by the system
and thus on the net solar energy price. Subsequently a genetic algorithm is employed to estimate the
optimum size of these two parameters, which maximise the net solar energy price, thus the design
time is reduced substantially and the solution obtained is more accurate that the trial and error
method used traditionally in these optimisations.
economic benefits. The system is modelled with TRNSYS computer program. An artificial neural
network is trained using a small number of annual TRNSYS simulation results, to learn the
correlation of collector area and storage tank size on the auxiliary energy required by the system
and thus on the net solar energy price. Subsequently a genetic algorithm is employed to estimate the
optimum size of these two parameters, which maximise the net solar energy price, thus the design
time is reduced substantially and the solution obtained is more accurate that the trial and error
method used traditionally in these optimisations.
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