Detection of snow-coverage on PV-modules with images based on CNN-techniques
| dc.contributor.author | Hepp, Dennis | |
| dc.contributor.author | Hempelmann, Sebastian | |
| dc.contributor.author | Behrens, Grit | |
| dc.contributor.author | Friedrich, Werner | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.contributor.editor | Naumann, Stefan | |
| dc.contributor.editor | Arndt, Hans-Knud | |
| dc.contributor.editor | Behrens, Grit | |
| dc.contributor.editor | Höb, Maximilian | |
| dc.date.accessioned | 2022-09-19T09:20:49Z | |
| dc.date.available | 2022-09-19T09:20:49Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The transition from fossil fuels to renewable energy is considered as very meaningful to mitigate climate change. To integrate weather-dependent energies firmly into the power grid, a forecast of the energy yield is very important. This paper is about renewable energy generation by photovoltaic (PV) systems. The yield of PV-systems depends not only on weather conditions, but in wintertime also on the additional factor “snow cover”. The aim of this work is to detect snow cover on photovoltaic plants to support the energy yield forecast. For this purpose, images of a PV-plant with and without snow cover are used for feature extraction and then analyzed by using a convolutional neural network (CNN). | en |
| dc.identifier.isbn | 978-3-88579-722-7 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39404 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | EnviroInfo 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-328 | |
| dc.subject | convolutional neural network | |
| dc.subject | machine learning | |
| dc.subject | python | |
| dc.subject | image recognition | |
| dc.subject | snowdetection | |
| dc.subject | photovoltaic | |
| dc.title | Detection of snow-coverage on PV-modules with images based on CNN-techniques | en |
| dc.type | Text/Conference Paper | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 123 | |
| gi.conference.date | 26.-30- September 2022 | |
| gi.conference.location | Hamburg |
Dateien
Originalbündel
1 - 1 von 1
Lade...
- Name:
- EnviroInfo2022_ShortPaper_47.pdf
- Größe:
- 833.84 KB
- Format:
- Adobe Portable Document Format