Please use this identifier to cite or link to this item:
|Title:||Artificial Neural Networks and Genetic Algorithms for the Optimisation of Solar Thermal Systems||Authors:||Kalogirou, Soteris A.||Keywords:||Artificial Neural Networks (ANN)
Solar thermal systems
|Issue Date:||2006||Source:||Proceedings of the IX World Renewable Energy Congress, Florence, Italy.||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.||Description:||This paper is published in the IX World Renewable Energy Congress, Florence, Italy.||URI:||http://ktisis.cut.ac.cy/handle/10488/824|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
Show full item record
checked on Jan 23, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.