Title: Global solar radiation estimation using artificial neural network by the addition of nearby meteorological stations' solar radiation data and exergy of solar radiation: a case study

Authors: Yusuf Kurtgoz; Emrah Deniz

Addresses: Graduate School of Natural and Applied Sciences, Department of Mechanical Engineering, Karabuk University, Demir-Celik Campus, 78050 Karabuk, Turkey ' Faculty of Engineering, Department of Mechanical Engineering, Karabuk University, Demir-Celik Campus, 78050 Karabuk, Turkey

Abstract: The artificial neural networks (ANNs) can be used to accurately predict the global solar radiation (GSR). There are many geographical, meteorological and terrestrial parameters affecting GSR. In this study, the most relevant of six input parameters are selected to predict the GSR of Goksun Station in Turkey using Waikato environment for knowledge analysis (Weka) Software. The effect of using nearby meteorological stations' GSR data as input on GSR prediction is investigated. Different ANN models are developed to demonstrate the difference between the exclusion and inclusion of these parameters on the model. The results show that the exclusion of less influential parameters and the inclusion of three nearby stations' GSR data has improved performance criteria. Petela, Spanner and Jeter's approaches are used for exergy analysis of measured and estimated GSR values. The mean exergy-to-energy ratio for both Petela and Spanner's approaches is 0.934, while Jeter's approach showed 0.950.

Keywords: GSR; global solar radiation estimation; solar energy; solar exergy; ANNs; artificial neural networks; Waikato environment for knowledge analysis; Weka; solar power; case study; Turkey; exergy analysis.

DOI: 10.1504/IJEX.2016.079309

International Journal of Exergy, 2016 Vol.21 No.3, pp.315 - 330

Received: 05 Jan 2016
Accepted: 12 Apr 2016
Published online: 26 Sep 2016 *

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