Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble

Unlike deterministic forecasts, probabilistic predictions provide estimates of uncertainty, which is an additional value for decision-making. Previous studies have proposed the analog ensemble (AnEn), which is a technique to generate uncertainty information from a purely deterministic forecast. The objective of this study is to improve the AnEn performance for wind power forecasts by developing static and dynamic weighting strategies, which optimize the predictor combination with a brute-force continuous ranked probability score (CRPS) minimization and a principal component analysis (PCA) of the predictors. Predictors are taken from the high-resolution deterministic forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF), including forecasts of wind at several heights, geopotential height, pressure, and temperature, among others. The weighting strategies are compared at five wind farms in Europe and the U.S. situated in regions with different terrain complexity, both on and offshore, and significantly improve the deterministic and probabilistic AnEn forecast performance compared to the AnEn with 10-m wind speed and direction as predictors and compared to PCA-based approaches. The AnEn methodology also provides reliable estimation of the forecast uncertainty. The optimized predictor combinations are strongly dependent on terrain complexity, local wind regimes, and atmospheric stratification. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, the AnEn performance is improved by up to 20‚ÄČ% at on and offshore sites.

To Access Resource:

Questions? Email Resource Support Contact:

  • opensky@ucar.edu
    UCAR/NCAR - Library

Resource Type publication
Temporal Range Begin N/A
Temporal Range End N/A
Temporal Resolution N/A
Bounding Box North Lat N/A
Bounding Box South Lat N/A
Bounding Box West Long N/A
Bounding Box East Long N/A
Spatial Representation N/A
Spatial Resolution N/A
Related Links N/A
Additional Information N/A
Resource Format N/A
Asset Size N/A
Legal Constraints

Copyright 2015 Schweizerbart Science Publishers.


Access Constraints None
Software Implementation Language N/A

Resource Support Name N/A
Resource Support Email opensky@ucar.edu
Resource Support Organization UCAR/NCAR - Library
Distributor N/A
Metadata Contact Name N/A
Metadata Contact Email opensky@ucar.edu
Metadata Contact Organization UCAR/NCAR - Library

Author Junk, Constantin
Delle Monache, Luca
Alessandrini, Stefano
Cervone, Guido
von Bremen, Lueder
Publisher UCAR/NCAR - Library
Publication Date 2015-07-21T00:00:00
Digital Object Identifier (DOI) Not Assigned
Alternate Identifier N/A
Resource Version N/A
Topic Category geoscientificInformation
Progress N/A
Metadata Date 2022-10-07T16:17:52.045881
Metadata Record Identifier edu.ucar.opensky::articles:16844
Metadata Language eng; USA
Suggested Citation Junk, Constantin, Delle Monache, Luca, Alessandrini, Stefano, Cervone, Guido, von Bremen, Lueder. (2015). Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d75b03qs. Accessed 25 March 2023.

Harvest Source