Comparison of different physical models for PV power output prediction

Solar Energy

Volumn 119, Issue , 2015, Pages 83-99

  Dolara, Alberto ^a   Leva, Sonia ^a   Manzolini, Giampaolo ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

NMAE; PV equivalent electrical circuit; PV forecast power production; SolarTechlab; WMAE

Indexed keywords

CALIBRATION; ELECTRIC POWER GENERATION; ERRORS; FORECASTING; NETWORKS (CIRCUITS); PHOTOVOLTAIC CELLS; THERMOELECTRIC POWER; THERMOGRAPHY (TEMPERATURE MEASUREMENT);

EQUIVALENT ELECTRICAL CIRCUITS; NMAE; POWER PRODUCTION; SOLARTECHLAB; WMAE;

WEATHER FORECASTING;

COMPARATIVE STUDY; ERROR ANALYSIS; EXPERIMENTAL STUDY; FUEL CELL; PHOTOVOLTAIC SYSTEM; PREDICTION; RENEWABLE RESOURCE;

EID: 84936876749     PISSN: 0038092X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.solener.2015.06.017     Document Type: Article

References (41)

1. Ahemd El Tayyan A. PV system behavior based on datasheet. J. Electron. Dev. 2011, 9:335-341.
2. Blair, N.J., Dobos, A.P., Gilman, P., 2013. Comparison of Photovoltaic Models in the System Advisor Model Preprint, (August).
3. Brooks, A.E., DellaGiustina, D.N., Patterson, S.M., Cronin, A.D., 2013. The consequence of soiling on PV system performance in Arizona; Comparing three study methods. In: 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC), pp. 0754-0758. doi:10.1109/PVSC.2013.6744259.
4. Cano, J., John, J.J., Tatapudi, S., TamizhMani, G., 2014. Effect of tilt angle on soiling of photovoltaic modules. In: 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), pp. 3174-3176. doi:10.1109/PVSC.2014.6925610.
5. Celik A.N. Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules. Sol. Energy 2011, 85(10):2507-2517. 10.1016/j.solener.2011.07.009.
6. Celik A.N., Acikgoz N. Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models. Appl. Energy 2007, 84(1):1-15. 10.1016/j.apenergy.2006.04.007.
7. Ciulla G., Lo Brano V., Di Dio V., Cipriani G. A comparison of different one-diode models for the representation of I-V characteristic of a PV cell. Renew. Sustain. Energy Rev. 2014, 32:684-696. 10.1016/j.rser.2014.01.027.
8. Cubas J., Pindado S., Victoria M. On the analytical approach for modeling photovoltaic systems behavior. J. Power Sources 2014, 247:467-474. 10.1016/j.jpowsour.2013.09.008.
9. De Blas M., Torres J., Prieto E., García A. Selecting a suitable model for characterizing photovoltaic devices. Renewable Energy 2002, 25(3):371-380. 10.1016/S0960-1481(01)00056-8.
10. De Soto W., Klein S.A., Beckman W.A. Improvement and validation of a model for photovoltaic array performance. Sol. Energy 2006, 80(1):78-88. 10.1016/j.solener.2005.06.010.
11. Dobos A.P. An improved coefficient calculator for the california energy commission 6 parameter photovoltaic module model. J. Sol. Energy Eng. 2012, 134(2):021011. 10.1115/1.4005759.
12. Duffie, J.A., Beckman, W.A., 1991. Solar Engineering of Thermal Processes, second ed. New York: John Wiley & Sons Inc.
13. Durgadevi, A., Arulselvi, S., Natarajan, S.P., 2011. Photovoltaic modeling and its characteristics. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology, pp. 469-475. doi:10.1109/ICETECT.2011.5760162.
14. Farret, F.A., Lenz, J.M., Trapp, J.G., 2011. New methodology to determinate photovoltaic parameters of solar panels. In: XI Brazilian Power Electronics Conference, pp. 275-279. doi:10.1109/COBEP.2011.6085281.
15. Fuentes K. A simplified thermal model of photovoltaic modules 1985, Albuquerque, NM, US.
16. Hocaoglu F., Gerek O., Kurban M. Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks. Sol. Energy 2008, 8:714-726.
17. Houabes, M., 2010. PV cell 5P modeling with shunt resistance correction. In: EFEEA 10th International Symposium on Environment Friendly Energies in Electrical Applications, pp. 1-5.
18. IEC60891, 2010. Photovoltaic Devices. Procedures for Temperature and Irradiance Corrections to Measured IV Characteristics. IEC 60891.
19. Izgi E., Oztopal A., Yerli B., Kaymak M., Sahin A. Short-mid-term solar power prediction by using artificial neural networks. Sol. Energy 2012, 86(2):725-733.
20. Laudani A., Mancilla-David F., Riganti-Fulginei F., Salvini A. Reduced-form of the photovoltaic five-parameter model for efficient computation of parameters. Sol. Energy 2013, 97:122-127. 10.1016/j.solener.2013.07.031.
21. Lejeune, T., Mermoud, A., 2010. Performance assessment of a simulation model for pv modules of any available, (September), pp. 6-10.
22. Lineykin S., Averbukh M., Kuperman A. An improved approach to extract the single-diode equivalent circuit parameters of a photovoltaic cell/panel. Renew. Sustain. Energy Rev. 2014, 30:282-289. 10.1016/j.rser.2013.10.015.
23. Lo Brano V., Orioli A., Ciulla G., Di Gangi A. An improved five-parameter model for photovoltaic modules. Sol. Energy Mater. Sol. Cells 2010, 94(8):1358-1370. 10.1016/j.solmat.2010.04.003.
24. Luque, A., Hegedus, S., 2013. Handbook of Photovoltaic Science and Engineering. (Wiley, Ed.).
25. Ma J., Man K.L., Ting T.O., Zhang N., Guan S.-U., Wong P.W.H. Approximate single-diode photovoltaic model for efficient I-V characteristics estimation. Sci. World J. 2013, 2013:230471. 10.1155/2013/230471.
26. Ma T., Yang H., Lu L. Development of a model to simulate the performance characteristics of crystalline silicon photovoltaic modules/strings/arrays. Sol. Energy 2014, 100:31-41. 10.1016/j.solener.2013.12.003.
27. Mellit A., Massi Pavan A. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy 2010, 84(5):807-821.
28. Monteiro, C., Fernandez-Jimenez, A., Ramirez-Rosadoc, I., Munoz-Jimenez, A., Lara-Santillan, P., 2013. Short-term forecasting models for photovoltaic plants: analytical versus soft-computing techniques. Mathematical Problems in Engineering, Article ID, 9.
29. Nelson J. The Physics of Solar Cells 2003, Imperial college press, London.
30. Ogliari E., Grimaccia F., Leva S., Mussetta M. Hybrid predictive models for accurate forecasting in PV systems. Energies 2013, 6(4):1929-2013.
31. Qasem, H., Betts, T.R., Mullegans, H., Al Busairi, H., Gottschaig, R., 2011. Dust effect on PV modules. In: Proceedings of the 7th photovoltaic science application and technology conference (PVSAT-7), Edinburgh, UK. http://www.pvsat.org.ukS.
32. Rodrigues, E., Melício, R., Mendes, V., Catalão, J., 2011. Simulation of a solar cell considering single-diode equivalent circuit model. In: International Conference on Renewable Energies and Power Quality. Spain, 13-15 April 2011.
33. Rus-Casas C., Aguilar J.D., Rodrigo P., Almonacid F., Pérez-Higueras P.J. Classification of methods for annual energy harvesting calculations of photovoltaic generators. Energ. Convers. Manage. 2014, 78:527-536. 10.1016/j.enconman.2013.11.006.
34. Sarver T., Al-Qaraghuli A., Kazmerski L.L. A comprehensive review of the impact of dust on the use of solar energy: history, investigations, results, literature, and mitigation approaches. Renew. Sustain. Energy Rev. 2013, 22:698-733. 10.1016/j.rser.2012.12.065.
35. Sera, D., Teodorescu, R., Rodriguez, P., 2007. PV panel model based on datasheet values. In: IEEE International Symposiums on Industrial Electronics, pp. 2392-2396.
36. Shongwe S., Hanif M. Comparative analysis of different single-diode PV modeling methods. IEEE J. Photovoltaics 2015, 5(3):938-946. 10.1109/JPHOTOV.2015.2395137.
37. SolarTech Lab, 2013. http://www.solartech.polimi.it.
38. Stridh, B., 2011. Evaluation of economical benefit of cleaning of soiling and snow in PV plants at three European locations, pp. 1448-1451.
39. Tossa A.K., Soro Y.M., Azoumah Y., Yamegueu D. A new approach to estimate the performance and energy productivity of photovoltaic modules in real operating conditions. Sol. Energy 2014, 110:543-560. 10.1016/j.solener.2014.09.043.
40. Ulbricht, R., Fisher, F., Lehner, W., Donker, H., 2013. First Steps Towards a Systematical Optimized Strategy for Solar Energy Supply Forecasting. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Prague, September 23th to 27 th.
41. Villalva M., Gazoli J., Filho E. Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 2009, 24:5.