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Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems

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  • Su, Yan
  • Chan, Lai-Cheong
  • Shu, Lianjie
  • Tsui, Kwok-Leung

Abstract

This paper develops new real time prediction models for output power and energy efficiency of solar photovoltaic (PV) systems. These models were validated using measured data of a grid-connected solar PV system in Macau. Both time frames based on yearly average and monthly average are considered. It is shown that the prediction model for the yearly/monthly average of the minutely output power fits the measured data very well with high value of R2. The online prediction model for system efficiency is based on the ratio of the predicted output power to the predicted solar irradiance. This ratio model is shown to be able to fit the intermediate phase (9am to 4pm) very well but not accurate for the growth and decay phases where the system efficiency is near zero. However, it can still serve as a useful purpose for practitioners as most PV systems work in the most efficient manner over this period. It is shown that the maximum monthly average minutely efficiency varies over a small range of 10.81% to 12.63% in different months with slightly higher efficiency in winter months.

Suggested Citation

  • Su, Yan & Chan, Lai-Cheong & Shu, Lianjie & Tsui, Kwok-Leung, 2012. "Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems," Applied Energy, Elsevier, vol. 93(C), pages 319-326.
  • Handle: RePEc:eee:appene:v:93:y:2012:i:c:p:319-326
    DOI: 10.1016/j.apenergy.2011.12.052
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    1. Al-Ismaily, Hilal A. & Probert, Douglas, 1998. "Photovoltaic electricity prospects in oman," Applied Energy, Elsevier, vol. 59(2-3), pages 97-124, February.
    2. Celik, Ali Naci & Acikgoz, NasIr, 2007. "Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models," Applied Energy, Elsevier, vol. 84(1), pages 1-15, January.
    3. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    4. Chow, T. T. & Chan, A. L. S., 2004. "Numerical study of desirable solar-collector orientations for the coastal region of South China," Applied Energy, Elsevier, vol. 79(3), pages 249-260, November.
    5. Cucumo, Mario & Rosa, Alessandro De & Ferraro, Vittorio & Kaliakatsos, Dimitrios & Marinelli, Valerio, 2006. "Performance analysis of a 3kW grid-connected photovoltaic plant," Renewable Energy, Elsevier, vol. 31(8), pages 1129-1138.
    6. Imtiaz Ashraf & A. Chandra, 2004. "Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 21(1/2), pages 119-130.
    7. Mellit, A. & Kalogirou, S.A. & Shaari, S. & Salhi, H. & Hadj Arab, A., 2008. "Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system," Renewable Energy, Elsevier, vol. 33(7), pages 1570-1590.
    8. Joyce, A. & Rodrigues, C. & Manso, R., 2001. "Modelling a PV system," Renewable Energy, Elsevier, vol. 22(1), pages 275-280.
    9. Ayompe, L.M. & Duffy, A. & McCormack, S.J. & Conlon, M., 2010. "Validated real-time energy models for small-scale grid-connected PV-systems," Energy, Elsevier, vol. 35(10), pages 4086-4091.
    10. So, Jung Hun & Jung, Young Seok & Yu, Gwon Jong & Choi, Ju Yeop & Choi, Jae Ho, 2007. "Performance results and analysis of 3kW grid-connected PV systems," Renewable Energy, Elsevier, vol. 32(11), pages 1858-1872.
    11. Hove, Tawanda, 2000. "A method for predicting long-term average performance of photovoltaic systems," Renewable Energy, Elsevier, vol. 21(2), pages 207-229.
    12. Zhou, Wei & Yang, Hongxing & Fang, Zhaohong, 2007. "A novel model for photovoltaic array performance prediction," Applied Energy, Elsevier, vol. 84(12), pages 1187-1198, December.
    Full references (including those not matched with items on IDEAS)

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