TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Gödde, Felix
A1  - Mayer, Bernhard
A1  - Zinner, Tobias
A1  - Witthuhn, Jonas
A1  - Deneke, Hartwig
A1  - Schirrmeister, Christopher
T1  - Predicting variability of horizontal surface solar irradiance using machine learning
T2  - EMS Annual Meeting Abstracts
N2  - Renewable energies play an increasingly important role for energy production in Europe. Unlike coal or gas powerplants, solar energy production is highly variable in space and time. This is due to the strong variability of cloudsand their influence on the surface solar irradiance. Especially in regions with large contribution from photovoltaicpower production, the intermittent energy feed-in to the power grid can be a risk for grid stability. Therefore goodforecasts of temporal and spatial variability of surface irradiance are necessary to be able to properly regulate thepower supply.
U6  - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-49959
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-49959
N1  - EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019, Copenhagen, Denmark, 9-13 September 2019
VL  - 16
PB  - Copernicus
CY  - Göttingen
ER  -