Accurate wind forecasts are of great importance for a wide range of areas, e.g., in the energy sector, in air traffic control, or for private leisure planning. Probabilistic predictions allow for a precise risk assessment and thus enable a well-founded decision making process. This dissertation develops different statistical approaches in the framework of distributional modeling to generate reliable probabilistic forecasts of wind speed and wind direction. The first presented article introduces the general concept of time-adaptive training schemes. Such schemes are typically required in the framework of distributional regression modeling to correctly post-process weather forecasts, including wind forecasts. In order to compare different time-adaptive training schemes in a uniform and simple setup, the study is carried out on post-processing near-surface air temperature forecasts across central Europe. Results indicate that approaches using data from multiple years for model training are often superior to approaches using the most recent days only. This is due to a more stable temporal evolution of the estimated coefficients, which leads to an overall improved performance; for the stations tested, this even holds in case of changes in the underlying ensemble prediction system which is employed for model training. Bridging the gap to probabilistic wind forecasting, the second paper presents a time-adaptive bivariate Gaussian regression model for the zonal and meridional wind components, employing EPS wind predictions as explanatory variables. The bivariate Gaussian process accounts for the circular characteristics of wind by utilizing both wind components and, therefore, information of wind speed and direction. In the third article, an alternative probabilistic modeling strategy is introduced by developing distributional regression trees and forests for a circular response, namely wind direction. Due to the highly volatile nature of wind direction, the tree-based distributional models prove to be most suitable for wind forecasting, as they can capture nonlinear changes, shifts, and potential interactions among covariates without pre-specifying such effects. In addition, as a natural extension, ensembles or forests of circular regression trees are shown to further enhance predictive performance by additionally regularizing and stabilizing the model.
Titelaufnahme
- TitelProbabilistic wind forecasting in the framework of distributional modeling / Moritz N. Lang
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- betreut von
- ErschienenInnsbruck, July 2020
- Umfangviii, 93 Seiten : Diagramme
- AnmerkungKumulative Dissertation aus drei Artikeln
- Datum der AbgabeJuli 2020
- SpracheEnglisch
- DokumenttypDissertation
- Schlagwörter (DE)
- Schlagwörter (EN)
- Schlagwörter (GND)
- URN
- Das Dokument ist online verfügbar
- Nachweis
Accurate wind forecasts are of great importance for a wide range of areas, e.g., in the energy sector, in air traffic control, or for private leisure planning. Probabilistic predictions allow for a precise risk assessment and thus enable a well-founded decision making process. This dissertation develops different statistical approaches in the framework of distributional modeling to generate reliable probabilistic forecasts of wind speed and wind direction. The first presented article introduces the general concept of time-adaptive training schemes. Such schemes are typically required in the framework of distributional regression modeling to correctly post-process weather forecasts, including wind forecasts. In order to compare different time-adaptive training schemes in a uniform and simple setup, the study is carried out on post-processing near-surface air temperature forecasts across central Europe. Results indicate that approaches using data from multiple years for model training are often superior to approaches using the most recent days only. This is due to a more stable temporal evolution of the estimated coefficients, which leads to an overall improved performance; for the stations tested, this even holds in case of changes in the underlying ensemble prediction system which is employed for model training. Bridging the gap to probabilistic wind forecasting, the second paper presents a time-adaptive bivariate Gaussian regression model for the zonal and meridional wind components, employing EPS wind predictions as explanatory variables. The bivariate Gaussian process accounts for the circular characteristics of wind by utilizing both wind components and, therefore, information of wind speed and direction. In the third article, an alternative probabilistic modeling strategy is introduced by developing
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