Urbich, I.; Bendix, J. & Müller, R.W. (2018): <b>A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo</b>. <i>Remote Sensing</i> <b>10</b>, 955.
Resource Description
Title:
A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo
FOR816dw ID:
318
Publication Date:
2018-06-15
License and Usage Rights:
Resource Owner(s):
Individual:
Isabel Urbich
Contact:
email:
webmaster <at> lcrs.de
Individual:
Jörg Bendix
Contact:
email:
bendix <at> staff.uni-marburg.de
Deutschhausstraße 12
Room No. 02 A 48
35032 Marburg
Faculty of Geography
Germany
Individual:
Richard W. Müller
Contact:
email:
webmaster <at> lcrs.de
Abstract:
The increasing use of renewable energies as a source of electricity has led to a fundamental<br/>
transition of the power supply system. The integration of ?uctuating weather-dependent energy<br/>
sources into the grid already has amajor impact on its load ?ows. As a result, the interest in forecasting<br/>
wind and solar radiation with a suf?cient accuracy over short time periods (<4 h) has grown. In this<br/>
study, the short-term forecast of the effective cloud albedo based on optical ?ow estimation methods<br/>
is investigated. The optical ?ow method utilized here is TV-L1 from the open source library OpenCV.<br/>
This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the<br/>
clouds are displaced, the solar surface radiation will be calculated with SPECMAGIC NOW, which<br/>
computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal<br/>
and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation<br/>
can be forecasted from 5 min up to 4 h with a resolution of 0.05. The validation results of this method<br/>
are very promising, and the RMSE of the 30-min, 60-min, 90-min and 120-min forecast equals 10.47%,<br/>
14.28%, 16.87% and 18.83%, respectively. The paper gives a brief description of the method for the<br/>
short-term forecast of the effective cloud albedo. Subsequently, evaluation results will be presented<br/>
and discussed.
Keywords:
| forecasting methods | cloud albedo |
Literature type specific fields:
ARTICLE
Journal:
Remote Sensing
Volume:
10
Page Range:
955
Metadata Provider:
Individual:
Jörg Bendix
Contact:
email:
bendix <at> staff.uni-marburg.de
Deutschhausstraße 12
Room No. 02 A 48
35032 Marburg
Faculty of Geography
Germany
