Baseline and Target Values for Regional and Point PV Power Forecasts: Toward Improved Solar Forecasting

Brian Hodge, Jie Zhang, Siyuan Lu, Hendrik Hamann, Brad Lehman, Joseph Simmonsd, Edwin Campos, Venkat Banunarayanan, Jon Black, John Tedescoh

Research output: Contribution to journalArticlepeer-review

66 Scopus Citations


Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. The financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.

Original languageAmerican English
Pages (from-to)804-819
Number of pages16
JournalSolar Energy
StatePublished - 2015

Bibliographical note

Publisher Copyright:
© 2015 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5D00-65285


  • Numerical weather prediction
  • Operating reserve
  • PV power forecasting
  • Ramp forecasting


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