Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning
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| Author: | Phil AupkeORCiD, Andreas KasslerORCiD, Andreas TheocharisORCiD, Magnus NilssonORCiD, Michael UelschenORCiD |
|---|---|
| Title (English): | Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning |
| URN: | urn:nbn:de:bsz:959-opus-68195 |
| DOI: | https://doi.org/10.3390/engproc2021005050 |
| Parent Title (English): | Engineering Proceedings |
| Publisher: | MDPI |
| Place of publication: | Basel Switzerland |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Completion: | 2021 |
| Release Date: | 2024/12/16 |
| Volume: | 5 |
| Issue: | 1 |
| Article Number: | 50 |
| Note: | 7th International conference on Time Series and Forecasting, 19–21 July 2021, Gran Canaria (Spain) |
| Faculties: | Fakultät IuI |
| DDC classes: | 000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
| Review Status: | Veröffentlichte Fassung/Verlagsversion |
| Collections: | Forschungsschwerpunkt / Nachhaltige Technologien und Prozesse |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |