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Photovoltaic (PV) performance modelling in the absence of onsite measured plane of array irradiance (POA) and module temperature

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  • Copper, J.K.
  • Sproul, A.B.
  • Jarnason, S.

Abstract

In this study, the outputs from a simple PV performance model were compared to measurements of AC power for three PV systems located across Sydney, Australia. The study aimed to investigate the level of uncertainty and bias of the model when onsite measurements of plane of array (POA) irradiance and module temperature were not available. The results demonstrated that the simple PV performance model estimated the AC performance with a low level of model bias (NBME = ±3.2%) and uncertainty (NRMSE < 6%) when onsite measurements of POA irradiance and module temperatures were available. For POA irradiance, the results indicated that modelling uncertainty increased significantly (NRMSE < 13%) when alternative methods to estimate POA irradiance were utilised. For module temperature, the results indicated that the choice of model coefficients had a significant impact on the performance of the module temperature models. In particular, for the three parallel roof mounted PV systems studied, the results suggested that the open rack/free standing or well ventilated module temperature coefficients should be used within the module temperature models investigated. This selection of coefficients was not directly evident given the PV systems investigated were parallel roof mounted PV systems, not free standing rack mounted arrays.

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  • Copper, J.K. & Sproul, A.B. & Jarnason, S., 2016. "Photovoltaic (PV) performance modelling in the absence of onsite measured plane of array irradiance (POA) and module temperature," Renewable Energy, Elsevier, vol. 86(C), pages 760-769.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:760-769
    DOI: 10.1016/j.renene.2015.09.005
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    References listed on IDEAS

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    Cited by:

    1. Adel Alblawi & M. H. Elkholy & M. Talaat, 2019. "ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance," Sustainability, MDPI, vol. 11(23), pages 1-24, November.
    2. Haghdadi, Navid & Copper, Jessie & Bruce, Anna & MacGill, Iain, 2017. "A method to estimate the location and orientation of distributed photovoltaic systems from their generation output data," Renewable Energy, Elsevier, vol. 108(C), pages 390-400.
    3. Nacer, T. & Hamidat, A. & Nadjemi, O. & Bey, M., 2016. "Feasibility study of grid connected photovoltaic system in family farms for electricity generation in rural areas," Renewable Energy, Elsevier, vol. 96(PA), pages 305-318.
    4. Alami Merrouni, Ahmed & Elwali Elalaoui, Fakhreddine & Mezrhab, Ahmed & Mezrhab, Abdelhamid & Ghennioui, Abdellatif, 2018. "Large scale PV sites selection by combining GIS and Analytical Hierarchy Process. Case study: Eastern Morocco," Renewable Energy, Elsevier, vol. 119(C), pages 863-873.
    5. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    6. Moslehi, Salim & Reddy, T. Agami & Katipamula, Srinivas, 2018. "Evaluation of data-driven models for predicting solar photovoltaics power output," Energy, Elsevier, vol. 142(C), pages 1057-1065.

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