ESTIMATION OF PV MODULE SURFACE TEMPERATURE USING ARTIFICIAL NEURAL NETWORKS
This study aimed to use the artificial neural network (ANN) method to estimate the surface temperature of a photovoltaic (PV) panel. Using the experimentally obtained PV data, the accuracy of the ANN model was evaluated. To train the artificial neural network (ANN), outer temperature solar radiation and wind speed values were inputs and surface temperature was an output. The ANN was used to estimate PV panel surface temperature. Using the Levenberg-Marquardt (LM) algorithm the feed forward artificial neural network was trained. Two back propagation type ANN algorithms were used and their performance was compared with the estimate from the LM algorithm. To train the artificial neural network, experimental data were used for two thirds with the remaining third used for testing. Additionally scaled conjugate gradient (SCG) back propagation and resilient back propagation (RB) type ANN algorithms were used for comparison with the LM algorithm. The performances of these three types of artificial neural network were compared and mean error rates of between 0.005962 and 0.012177% were obtained. The best estimate was produced by the LM algorithm. Estimation of PV surface temperature with artificial neural networks provides better results than conventional correlation methods. This study showed that artificial neural networks may be effectively used to estimate PV surface temperature.
Jones, A.D., Underwood, C.P., "A thermal model for photovoltaic systems", Solar Energy, 70(4), 349–359, 2001.
Alonso Garcı, M.C., Balenzategui, J.L., "Estimation ofphotovoltaic module yearly temperature and performance based on Nominal Operation Cell Temperature calculations", Renewable Energy, 29, 1997–2010, 2004.
Skoplaki, E., Palyvos, J.A., "Operating temperature of photovoltaic modules: A survey of pertinent correlations" Renewable Energy, 34, 23–29, 2009.
Schott, T., "Operation temperatures of PV modules", In: Proceedings of the sixth E.C. photovoltaic solar energy conference, London, UK, April 15–19; p. 392–6, 1985.
Servant, J.M., "Calculation of the cell temperature for photovoltaic modules from climatic data", In: Bilgen E, Hollands KGT, editors. Proceedings of the 9th biennial congress of ISES – Intersol 85, Montreal, Canada, extended abstracts, p. 370, 1985.
Duffie, J.A, Beckman, W.A., "Solar energy thermal processes", 2nd ed. Hoboken (NJ): Wiley; 1991.
Tiwari, GN., Solar energy – fundamentals, design, modelling and applications. Pangbourne (UK): Alpha Science; 2002. p. 450.
Hove, T., "A method for predicting long-term average performance of photovoltaic systems", Renewable Energy, 21, 207–29, 2000.
Del Cueto, J.A., "Model for the thermal characteristics of flat-plate photovoltaic modules deployed at fixed tilt", In: Proceedings of the 28th IEEE photovoltaic specialists conference, Anchorage, AL, September 15–22; p. 1441–5, 2000.
Kou, Q., Klein, S.A., Beckman, W.A., "A method for estimating the long-term performance of direct-coupled PV pumping systems", Solar Energy, 64, 33–40, 1998.
Eicker, U., "Solar technologies for buildings", Chichester (UK): Wiley; 2003. Section 5.9.
Tiwari, A., Sodha, M.S., "Performance evaluation of a solar PV/T system: an experimental validation", Solar Energy, 80, 751–9, 2006.
Tiwari, A., Sodha, M.S., "Performance evaluation of a solar PV/T system: a parametric study", Renewable Energy 31, 2460–74, 2006.
ASTM. Method for determining the nominal operating cell temperature (NOCT) of an array or module. E1036M, Annex A.1., p. 544, 1999 (withdrawn recently).
Duffie, J.A., Beckman, W.A., "Solar energy thermal processes", 3rd ed. Hoboken (NJ): Wiley; 2006.
Davis, M.W., Dougherty, B.P., Fanney, A.H., "Prediction of building integrated photovoltaic cell temperatures", Transactions of the ASME – Journal of Solar Energy Engineering, 123, 200–10, 2001.
Kocyigit, N., "Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network", Int. J. Refrigeration, 50, 69-79, 2015.