Artificial neural networks for the performance prediction of large solar systems

S.A. Kalogirou, E. Mathioulakis and V. Belessiotis

Renewable Energy, 2014, vol. 63, issue C, 90-97

Abstract: In this paper, artificial neural networks (ANNs) are used for the performance prediction of large solar systems. The ANN method is used to predict the expected daily energy output for typical operating conditions, as well as the temperature level the storage tank can reach by the end of the daily operation cycle. These are considered as the most important parameters for the user. Experimental measurements from almost one year (226 days) have been used to investigate the ability of ANN to model the energy behavior of a typical large solar system. From the results, it can be concluded that the ANN effectively predicts the daily energy performance of the system; the statistical R2-value obtained for the training and validation data sets was better than 0.95 and 0.96 for the two performance parameters respectively. The data used in the validation were completely unknown to the ANN, which proves the ability of the ANN to give good predictions on completely unknown data. The results obtained from the method were also compared to the input–output model predictions with good accuracy whereas multiple linear regression could not give as accurate results. Additionally, the network was used with various combinations of input parameters and gave results of the same order of magnitude as the suggested method, which prove the robustness of the method. The advantages of the proposed approach include the simplicity in the implementation, even when the characteristics of the system components are not known, as well as the potential to improve the capability of the ANN to predict the performance of the solar system, through the continuous addition of new data collected during the operation of the system.

Keywords: Artificial neural networks; Large solar thermal systems; Daily energy output; Maximum temperature of storage tank (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:63:y:2014:i:c:p:90-97

DOI: 10.1016/j.renene.2013.08.049

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