Probabilistic Solar Forecasting Using Quantile Regression Models

Philippe Lauret, Mathieu David and Hugo T. C. Pedro
Additional contact information
Philippe Lauret: PIMENT Laboratory, Université de La Réunion,15 Avenue René Cassin, 97715 Saint-Denis, France
Mathieu David: PIMENT Laboratory, Université de La Réunion,15 Avenue René Cassin, 97715 Saint-Denis, France
Hugo T. C. Pedro: Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Energy Research University of California, San Diego, La Jolla, CA 92093, USA

Energies, 2017, vol. 10, issue 10, 1-17

Abstract: 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.

Keywords: probabilistic solar forecasting; quantile regression; ECMWF; reliability; sharpness; CRPS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/10/1591/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/10/1591/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:10:p:1591-:d:114807

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().