 Modelling of an ICS solar water heater using artificial neural networks and TRNSYSM. Souliotis, S. Kalogirou and Y. Tripanagnostopoulos Renewable Energy, 2009, vol. 34, issue 5, 1333-1339 Abstract: A study, in which a suitable artificial neural network (ANN) and TRNSYS are combined in order to predict the performance of an Integrated Collector Storage (ICS) prototype, is presented. Experimental data that have been collected from outdoor tests of an ICS solar water heater with cylindrical water storage tank inside a CPC reflector trough were used to train the ANN. The ANN is then used through the Excel interface (Type 62) in TRNSYS to model the annual performance of the system by running the model with the values of a typical meteorological year for Athens, Greece. In this way the specific capabilities of both approaches are combined, i.e., use of the radiation processing and modelling power of TRNSYS together with the “black box” modelling approach of ANNs. The details of the calculation steps of both methods that aim to perform an accurate prediction of the system performance are presented and it is shown that this new method can be used effectively for such predictions. Keywords: Solar water heaters; Integrated Collector Storage systems; Artificial neural networks; TRNSYS (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:34:y:2009:i:5:p:1333-1339 DOI: 10.1016/j.renene.2008.09.007 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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