Advances in Geosciences
Advances in Geosciences
ADGEO
Articles | Volume 45
Articles | Volume 45
https://doi.org/10.5194/adgeo-45-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/adgeo-45-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 45
19 Jul 2018
 | 19 Jul 2018

Probabilistic short term wind power forecasts using deep neural networks with discrete target classes

Martin Felder, Frank Sehnke, Kay Ohnmeiß, Leon Schröder, Constantin Junk, and Anton Kaifel

Viewed

Total article views: 2,526 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,916 484 126 2,526 69 69
  • HTML: 1,916
  • PDF: 484
  • XML: 126
  • Total: 2,526
  • BibTeX: 69
  • EndNote: 69
Views and downloads (calculated since 19 Jul 2018)
Cumulative views and downloads (calculated since 19 Jul 2018)

Viewed (geographical distribution)

Total article views: 2,370 (including HTML, PDF, and XML) Thereof 2,327 with geography defined and 43 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Apr 2025
Download
Short summary
In current applications of short term wind power forecasts, reliability information for the forecast is important because it helps decision making. Forecast errors are often estimated using ensembles of slightly disturbed weather predictions. Since this is very computation intensive, we propose a new method: A Deep Neural Network learns how reliable to forecast is, by comparing the current weather forecast with historical forecasts and the corresponding wind power production.
Read more
Share