Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

Brian Hodge, Jie Zhang, Anthony Florita, WanYin Cheung, Siyuan Lu, Hendrik Hamann, Qian Sun, Brad Lehman

Research output: Contribution to conferencePaper


Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.
Original languageAmerican English
Number of pages8
StatePublished - 2015
Event5th International Workshop on Integration of Solar Power Into Power Systems - Brussels, Belgium
Duration: 19 Oct 201520 Oct 2015


Conference5th International Workshop on Integration of Solar Power Into Power Systems
CityBrussels, Belgium

NREL Publication Number

  • NREL/CP-5D00-64960


  • machine learning
  • National Renewable Energy Laboratory (NREL)
  • NREL
  • numerical weather prediction
  • sensitivity analysis
  • solar forecasting
  • uncertainty


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