Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors

Vincent P.A. Lonij, Adria E. Brooks, Alexander D. Cronin, Michael Leuthold, Kevin Koch

Research output: Contribution to journalArticlepeer-review

126 Scopus citations


We report a new method to forecast power output from photovoltaic (PV) systems under cloudy skies that uses measurements from ground-based irradiance sensors as an input. This work describes an implementation of this forecasting method in the Tucson, AZ region where we use 80 residential rooftop PV systems distributed over a 50. km. ×. 50. km area as irradiance sensors. We report RMS and mean bias errors for a one year period of operation and compare our results to the persistence model as well as forecasts from other authors. We also present a general framework to model station-pair correlations of intermittency due to clouds that reproduces the observations in this work as well as those of other authors. Our framework is able to describe the RMS errors of velocimetry based forecasting methods over three orders of magnitude in the forecast horizon (from 30. s to 6. h). Finally, we use this framework to recommend optimal locations of irradiance sensors in future implementations of our forecasting method.

Original languageEnglish (US)
Pages (from-to)58-66
Number of pages9
JournalSolar energy
StatePublished - Nov 2013


  • Cloud forecasting
  • Irradiance
  • Spatio-temporal correlation
  • Station-pair correlation
  • Variability

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Materials Science


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