Probabilistic Solar Forecasting Using Quantile Regression Models
AbstractIn 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
Share & Cite This Article
Lauret, P.; David, M.; Pedro, H.T.C. Probabilistic Solar Forecasting Using Quantile Regression Models. Energies 2017, 10, 1591.
Lauret P, David M, Pedro HTC. Probabilistic Solar Forecasting Using Quantile Regression Models. Energies. 2017; 10(10):1591.Chicago/Turabian Style
Lauret, Philippe; David, Mathieu; Pedro, Hugo T.C. 2017. "Probabilistic Solar Forecasting Using Quantile Regression Models." Energies 10, no. 10: 1591.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.