ICS solar water heater study using artificial neural networks
Date Issued
June 2006
Abstract
In this paper we present 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.
We use the 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, to train the ANN. The ANN is then
used though 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. We present
the details of the calculation steps of both methods that aim to the accurate prediction of the system
performance and we show that this new method can be used effectively for such predictions
are combined in order to predict the performance of an Integrated Collector Storage (ICS) prototype.
We use the 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, to train the ANN. The ANN is then
used though 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. We present
the details of the calculation steps of both methods that aim to the accurate prediction of the system
performance and we show that this new method can be used effectively for such predictions
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