Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/136
Title: Prediction of flat-plate collector performance parameters using artificial neural networks
Authors: Kalogirou, Soteris A. 
Keywords: Artificial Neural Networks (ANN)
Flat-plate solar collectors
Performance parameters prediction
Issue Date: 2006
Publisher: Elsevier B. V.
Source: Solar Energy, Vol. 80, no. 3, 2006, pp. 248-259
Abstract: The objective of this work is to use Artificial Neural Networks (ANN) for the prediction of the performance parameters of flat-plate solar collectors. ANNs have been used in diverse applications and they have been shown to be particularly useful in system modeling and system identification. Six ANN models have been developed for the prediction of the standard performance collector equation coefficients, both at wind and no-wind conditions, the incidence angle modifier coefficients at longitudinal and transverse directions, the collector time constant, the collector stagnation temperature and the collector heat capacity. Different networks were used due to the different nature of the input and output required in each case. The data used for the training, testing and validation of the networks were obtained from the LTS database. The results obtained when unknown data were presented to the networks are very satisfactory and indicate that the proposed method can successfully be used for the prediction of the performance parameters of flat-plate solar collectors. The advantages of this approach compared to the conventional testing methods are speed, simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network.
URI: http://ktisis.cut.ac.cy/handle/10488/136
ISSN: 0038-092X
DOI: 10.1016/j.solener.2005.03.003
Rights: Copyright © 2005 Elsevier Ltd All rights reserved.
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