Összesen 1 találat.


001-es BibID:BIBFORM117273
035-os BibID:(WoS)001132707300001 (Scopus)85180898277
Első szerző:Baran Ágnes (matematikus)
Cím:A two-step machine learning approach to statistical post-processing of weather forecasts for power generation / Ágnes Baran, Sándor Baran
ISSN:0035-9009 1477-870X
Megjegyzések:By the end of 2021, the renewable energy share of the global electricity capacity reached 38.3% and the new installations are dominated by wind and solar energy, showing global increases of 12.7% and 18.5%, respectively. However, both wind and photovoltaic energy sources are highly volatile making planning difficult for grid operators, so accurate forecasts of the corresponding weather variables are essential for reliable electricity predictions. The most advanced approach in weather prediction is the ensemble method, which opens the door for probabilistic forecasting; though ensemble forecast are often underdispersive and subject to systematic bias. Hence, they require some form of statistical post-processing, where parametric models provide full predictive distributions of the weather variables at hand. We propose a general two-step machine learning-based approach to calibrating ensemble weather forecasts, where in the first step improved point forecasts are generated, which are then together with various ensemble statistics serve as input features of the neural network estimating the parameters of the predictive distribution. In two case studies based of 100m wind speed and global horizontal irradiance forecasts of the operational ensemble prediction system of the Hungarian Meteorological Service, the predictive performance of this novel method is compared with the forecast skill of the raw ensemble and the state-of-the-art parametric approaches. Both case studies confirm that at least up to 48h statistical post-processing substantially improves the predictive performance of the raw ensemble for all considered forecast horizons. The investigated variants of the proposed two-step method outperform in skill their competitors and the suggested new approach is well applicable for different weather quantities and for a fair range of predictive distributions.
Tárgyszavak:Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban
1D convolutional neural network
ensemble calibration
ensemble model output statistics
multilayer perceptron
solar irradiance
wind speed
Megjelenés:Quarterly Journal Of The Royal Meteorological Society. - 150 : 759 (2024), p. 1029-1047. -
További szerzők:Baran Sándor (1973-) (matematikus, informatikus)
Pályázati támogatás:K142849
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat
Rekordok letöltése1