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Energies 2017, 10(10), 1591;

Probabilistic Solar Forecasting Using Quantile Regression Models

PIMENT Laboratory, Université de La Réunion,15 Avenue René Cassin, 97715 Saint-Denis, France
Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Energy Research University of California, San Diego, La Jolla, CA 92093, USA
Author to whom correspondence should be addressed.
Received: 11 September 2017 / Revised: 3 October 2017 / Accepted: 6 October 2017 / Published: 13 October 2017
(This article belongs to the Special Issue Solar Forecasting)
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In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS), a Numerical Weather Prediction (NWP) model maintained by the European Center for Medium-Range Weather Forecast (ECMWF). Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions. View Full-Text
Keywords: probabilistic solar forecasting; quantile regression; ECMWF; reliability; sharpness; CRPS probabilistic solar forecasting; quantile regression; ECMWF; reliability; sharpness; CRPS

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Lauret, P.; David, M.; Pedro, H.T.C. Probabilistic Solar Forecasting Using Quantile Regression Models. Energies 2017, 10, 1591.

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