Validation of Subhourly Clipping Loss Error Corrections: Preprint

Abhishek Parikh, Kirsten Perry, Kevin Anderson, William Hobbs, Rounak Kharait, Mark Mikofski

Research output: Contribution to conferencePaper


Under-performance of solar PV systems is an important issue that increases risks for stakeholders, including developers, investors and operators. Recently some attention has focused on underestimation of inverter clipping losses as a possible source of over-prediction where sub-hourly solar variability is high. Several models and data sets have been analyzed over the past few years, with the aim of quantifying, predicting, and correcting underestimated clipping loss errors for systems with high DC/AC ratio and solar variability. In this research, we apply a machine learning model developed at NREL to two physical PV systems, to correct for subhourly clipping losses. For each system, we compare overall AC power output for the model taken at 1-minute intervals to AC power output taken at 1-hour intervals with the addition of the subhourly clipping correction. Our findings consistently show that the addition of the clipping loss correction lead to a reduction in mean bias error of 0.6\% and 1.1\% for systems A and B, respectively, with no additional filtering applied. When examining high solar variability periods where clipping is more pronounced, system A and B experienced a 1.4\% and 2.5\% reduction in mean bias error, respectively, when the clipping correction was applied.
Original languageAmerican English
Number of pages9
StatePublished - 2021
Event48th IEEE Photovoltaic Specialists Conference (PVSC 48) -
Duration: 20 Jun 202125 Jun 2021


Conference48th IEEE Photovoltaic Specialists Conference (PVSC 48)

Bibliographical note

See NREL/CP-5K00-81321 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5K00-80245


  • machine learning
  • photovoltaic
  • subhourly clipping


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