Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

Kate Doubleday, Stephen Jascourt, William Kleiber, Bri-Mathias Hodge

Research output: Contribution to journalArticlepeer-review

59 Scopus Citations


There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2-36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

Original languageAmerican English
Article number9091068
Pages (from-to)325-337
Number of pages13
JournalIEEE Transactions on Sustainable Energy
Issue number1
StatePublished - Jan 2021

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-74019


  • Bayesian model averaging (BMA)
  • Beta distribution
  • Probabilistic fore-casting
  • Solar power clipping
  • Solar power forecasting


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