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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
Dátum:	2024
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 folyóiratcikk 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 OTKA
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM088189
035-os BibID:	(WoS)000560814600001 (Scopus)85089706946
Első szerző:	Baran Sándor (matematikus, informatikus)
Cím:	Statistical post-processing of heat index ensemble forecasts: Is there a royal road? / Sándor Baran, Ágnes Baran, Florian Pappenberger, Zied Ben Bouallegue
Dátum:	2020
ISSN:	0035-9009 1477-870X
Megjegyzések:	We investigate the effect of statistical post?processing on the probabilistic skill of discomfort index (DI) and indoor wet?bulb globe temperature (WBGTid) ensemble forecasts, both calculated from the corresponding forecasts of temperature and dew point temperature. Two different methodological approaches to calibration are compared. In the first case, we start with joint post?processing of the temperature and dew point forecasts and then create calibrated samples of DI and WBGTid using samples from the obtained bivariate predictive distributions. This approach is compared with direct post?processing of the heat index ensemble forecasts. For this purpose, a novel ensemble model output statistics model based on a generalized extreme value distribution is proposed. The predictive performance of both methods is tested on the operational temperature and dew point ensemble forecasts of the European Centre for Medium?Range Weather Forecasts and the corresponding forecasts of DI and WBGTid. For short lead times (up to day 6), both approaches significantly improve the forecast skill. Among the competing post?processing methods, direct calibration of heat indices exhibits the best predictive performance, very closely followed by the more general approach based on joint calibration of temperature and dew point temperature. Additionally, a machine learning approach is tested and shows comparable performance for the case when one is interested only in forecasting heat index warning level categories.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Quarterly Journal Of The Royal Meteorological Society. - 146 : 732 (2020), p. 3416-3434. -
További szerzők:	Baran Ágnes (1972-) (matematikus) Pappenberger, Florian Ben Bouallègue, Zied
Pályázati támogatás:	NKFIH NN125679 Egyéb EFOP-3.6.2-16-2017-00015 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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