Quantifying and Reducing Curve-Fitting Uncertainty in Isc: Preprint

Keith Emery, Benjamin Duck

Research output: Contribution to conferencePaper


Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.
Original languageAmerican English
Number of pages8
StatePublished - 2015
Event42nd IEEE Photovoltaic Specialists Conference - New Orleans, Louisiana
Duration: 14 Jun 201519 Jun 2015


Conference42nd IEEE Photovoltaic Specialists Conference
CityNew Orleans, Louisiana

NREL Publication Number

  • NREL/CP-5J00-63602


  • Bayesian inference
  • data window selection
  • evidence
  • linear regression
  • measurement uncertainty analysis
  • model discrepancy
  • noise model
  • uncertainty quantification


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