Pregled bibliografske jedinice broj: 766631
Predictor-Corrector Method for Weather Forecast Improvement using Local Measurements
Predictor-Corrector Method for Weather Forecast Improvement using Local Measurements // Proceedings of the 18th International Conference on Electrical Drives and Power Electronics, EDPE 2015
Vysoké Tatry, Slovačka, 2015. str. 167-172 doi:10.1109/EDPE.2015.7325289 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 766631 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Predictor-Corrector Method for Weather Forecast
Improvement using Local Measurements
Autori
Gulin, Marko ; Vašak, Mario ; Matuško, Jadranko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 18th International Conference on Electrical Drives and Power Electronics, EDPE 2015
/ - , 2015, 167-172
Skup
18th International Conference on Electrical Drives and Power Electronics, EDPE
Mjesto i datum
Vysoké Tatry, Slovačka, 21.09.2015. - 23.09.2015
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Predictor-Corrector Method ; Weather Forecast ; Local Measurements ; Air Temperature Prediction ; Microgrid
Sažetak
Weather forecast is a crucial input for prediction of local building consumption and power production profiles in the building’s microgrid. E.g., prediction of solar irradiance components and air temperature is used to predict photovoltaic array power production, while air temperature and humidity are often used to predict building consumption during the day. Due to the computation complexity of meteorological models, new prediction sequence becomes available every 6 h at best, and often comes with a nearly 4 h lag. In this paper we develop a linear and nonlinear corrector models to improve weather forecast by using local measurements only. The main motivation behind this approach is to correct prediction sequence by using local measurements as they become available, i.e. prediction sequence is refreshed every 1 h instead of every 6 h. The proposed approach is validated on historical air temperature prediction sequences and actual measurements during 6 months period.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Temeljne tehničke znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb