Abstract
Due to various seasonal, hourly and daily changes in climate, it is relatively difficult to find a suitable analytic model for predicting the output power of Grid-Connected Photovoltaic (GCPV) plants. In this chapter, a simplified artificial neural network configuration is used for estimating the power produced by a 20kWp GCPV plant installed at Trieste, Italy. A database of experimentally measured climate (irradiance and air temperature) and electrical data (power delivered to the grid) for nine months is used. Four Multilayer-perceptron (MLP) models have been investigated in order to estimate the energy produced by the GCPV plant in question. The best MLP model has as inputs the solar irradiance and module temperature. The results show that good effectiveness is obtained between the measured and predicted power produced by the 20kWp GCPV plant. The developed model has been compared with different existing regression polynomial models in order to show its effectiveness. Three performance parameters that define the overall system performance with respect to the energy production, solar resource, and overall effect of system losses are the final PV system yield, reference yield and performance ratio.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
De Soto, W., Klein, S.A., Beckman, W.A.: Improvement and validation of a model for photovoltaic array performance. Solar Energy 80, 78–88 (2006)
Gianolli-Rossi, E., Krebs, K.: Energy rating of PV modules by outdoor response analysis. In: 8th E.C. PV Solar Energy Conference, Florence, Italy (1988)
Gupta, M.M., Liang, J., Noriyasu, H.: Static and dynamic neural networks. In: Zadeh, L.A. (ed.) Fundamentals to Advanced Theory. IEEE press, John Wiley and Sons Ltd. (2003)
Haykin, S.: Neural networks: A comprehensive foundation, 2nd edn. Macmillan, Basingstoke (1999)
IEA (2006) Trends in photovoltaic applications. Survey report of selected IEA countries between 1992 and 2006. International Energy Agency
IEC - CEI EN Std. 61724, Photovoltaic system performance monitoring - Guidelines for measurement, data exchange and analysis, International Electro-technical Commission (1999)
IEC 61724, Photovoltaic System Performance Monitoring - Guidelines for Measurement, Data Exchange and Analysis (1998)
Kalogirou, S.A.: Artificial Neural Networks in Renewable Energy Systems: A Review. Renewable & Sustainable Energy Reviews 5(4), 373–401 (2001)
King, D.L., Kratochvil, J.A., Boyson, W.E., Bower, W.I.: Field Experience with a New Performance Characterization Procedure for Photovoltaic Arrays. Presented at the 2nd World Conference and Exhibition on Photovoltaic Solar Energy Conversion, Vienna, Austria, July 6-10 (1998)
Lakhmi, C.J., Martin, N.M.: Fusion of neural networks, fuzzy systems and genetic algorithms: Industrial Applications. CRC Press, LLC, Boca Raton (1998)
Livingstone, D.J.: Artificial neural networks: Methods and Applications. Humana Press (2008)
Lughi, V., Massi Pavan, A., Quaia, S., Sulligoi, G.: Economical analysis and innovative solutions for grid connected PV plants. In: Proceeding of Speedam, International Conference on Power Electronics, Electrical Drives. Automation and Motion, Ischia (Italy), June 11-13, pp. 211–216 (2008)
Marion, A., Adelstein, J., Boyle, K., Hayden, H., Hammond, B., Fletcher, T., Canada Narang, B., Shugar, D., Wenger, D., Kimber, H., Mitchell, A., Rich, L., Townsend, G.: Performance parameters for grid-connected PV systems. In: 31st IEEE Photovoltaics Specialist Conference and Exhibition, Lake Buena Vista, Florida (2005)
Mavromatakis, F., Makrides, G., Georghiou, G., Pothrakis, A., Franghiadakis, Y., Drakakis, E., Koudoumas, E.: Modeling the photovoltaic potential of a site. Renewable Energy 35, 1387–1390 (2010)
Mayer, D., Wald, L., Poissant, Y., Pelland, S.: Performance prediction of grid-connected photovoltaic systems using remote sensing. Report IEA-PVPS T2-07, p. 18, www.trpvplatform.org
Mellit, A.: Recurrent neural network-based forecasting of the daily electricity generation of a Photovoltaic power system. In: Ecological Vehicle and Renewable Energy (EVER), Monaco, March 26-29, pp. 265–270 (2009)
Mellit, A., Kalogirou, S.A.: Artificial intelligence techniques for photovoltaic applications: A review. Progress in Energy and Combustion Science 34, 547–632 (2008)
Mellit, A., Massi Pavan, A.: A 24-hours forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy 84, 807–821 (2010a)
Mellit, A., Massi Pavan, A.: Performance prediction of 20kWp grid-connected photovoltaic plant at Trieste (Italy) using artificial neural network. Energy Conversion and Management 51, 2431–2441 (2010b)
Menicucci, D., Fernandez, J.P.: User’s manual for PVFORM: a photovoltaic system simulation program for stand-alone and grid interactive applications; Sandia National Laboratories, SAND85-0376, Albuquerque, USA (1988)
Meyer, E.L., van Dyk, E.E.: Development of energy model based on total daily irradiation and maximum ambient temperature. Renewable Energy 21, 37–47 (2000)
PVUSA Technical Speci®cation, 19271-EMT-3 (August 1991)
Pavan, M.A., Castellan, S., Quaia, S., Roitti, S., Sulligoi, G.: Power Electronic Conditioning Systems for Industrial Photovoltaic Systems: Centralized or String Inverters? In: Proc. of ICCEP Int. Conf. on Clean Electrical Power, Capri (Italy), May 21-23, pp. 208–214 (2007)
Pavan, M.A., Lughi, V., Roitti, S., Mellit, A.: Impiego di reti neurali per la previsione dell’irradianza solare e della potenza prodotta da un impianto fotovoltaico, 10° Convegno Nazionale AIMAT, Capo Vaticano (VV), September 5-8 (2010)
Rosell, J.I., Ibanez, M.: Modelling power output in photovoltaic modules for outdoor operating conditions. Energy Conversion and Management 47, 2424–2430 (2006)
Whitaker, C., Townsend, T.U., Newmiller, J.D., King, D.L., Boyson, W.E., Kratochvil Collier, J.A., Osborn, D.E.: Application and validation of a new PV performance characterization method. In: Proceedings of the 26th IEEE PV Specialist Conference, Anaheim, USA (1997)
Yu, H.H., Jenq-Neng, H.: Handbook of neural network signal processing. CRC Press, Boca Raton (2001)
Zhou, W., Yang, H., Fang, Z.: A novel model for photovoltaic array performance prediction. Applied Energy 84, 1187–1198 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Mellit, A., Pavan, A.M., Kalogirou, S.A. (2011). Application of Artificial Neural Networks for the Prediction of a 20-kWp Grid-Connected Photovoltaic Plant Power Output. In: Gopalakrishnan, K., Khaitan, S.K., Kalogirou, S. (eds) Soft Computing in Green and Renewable Energy Systems. Studies in Fuzziness and Soft Computing, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22176-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-22176-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22175-0
Online ISBN: 978-3-642-22176-7
eBook Packages: EngineeringEngineering (R0)