Validation of Subhourly Clipping Loss Error Corrections

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

Research output: Contribution to conferencePaperpeer-review

6 Scopus Citations


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 0.8% and 1.2% 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 1.8% and 2.7% reduction in mean bias error, respectively, when the clipping correction was applied.

Original languageAmerican English
Number of pages6
StatePublished - 20 Jun 2021
Event48th IEEE Photovoltaic Specialists Conference, PVSC 2021 - Fort Lauderdale, United States
Duration: 20 Jun 202125 Jun 2021


Conference48th IEEE Photovoltaic Specialists Conference, PVSC 2021
Country/TerritoryUnited States
CityFort Lauderdale

Bibliographical note

See NREL/CP-5K00-80245 for preprint

NREL Publication Number

  • NREL/CP-5K00-81321


  • clipping
  • inverter
  • irradiance
  • modeling
  • performance
  • solar
  • TMY
  • variability


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