Predicting power ramps from joint distributions of future wind speeds

Power ramps are sudden changes in turbine power and must be accurately predicted to minimize costly imbalances in the electrical grid. Doing so requires reliable wind speed forecasts, which can be obtained from ensembles of physical numerical weather prediction (NWP) models through statistical postprocessing.

Circular regression trees and forests with an application to probabilistic wind direction forecasting

Introduction of distributional regression trees and random forests for circular responses, yielding probabilistic forecasts based on the von Mises distribution. The proposed methodology is evaluated in a case study on probabilistic wind direction forecasting at two Austrian airports, considering other common approaches as a benchmark.

A case study of empirical copula methods for the statistical correction of forecasts of the ALADIN-LAEF system

Statistical post-processing with standardized anomalies based on a 1 km gridded analysis

Remember the past: A comparison of time-adaptive training schemes for non-homogeneous regression

Comparison of various time-adaptive training schemes with the classical sliding training window approach for the application of post-processing near-surface air temperature forecasts across Central Europe. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only.

Bivariate Gaussian models for wind vectors in a distributional regression framework

On the vertical exchange of heat, mass, and momentum over complex, mountainous terrain

The impact of embedded valleys on daytime pollution transport over a mountain range