Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
| dc.contributor.author | Schreiber, Jens | |
| dc.contributor.author | Buschin, Artjom | |
| dc.contributor.author | Sick, Bernhard | |
| dc.contributor.editor | David, Klaus | |
| dc.contributor.editor | Geihs, Kurt | |
| dc.contributor.editor | Lange, Martin | |
| dc.contributor.editor | Stumme, Gerd | |
| dc.date.accessioned | 2019-08-27T12:55:31Z | |
| dc.date.available | 2019-08-27T12:55:31Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected forecast horizons. However, these estimates allow only a limited and highly uncertain use in the planning of electric power distribution. More reliable planning processes require considerably more information about future forecast quality. In this article, we present an in-depth analysis and comparison of influencing factors regarding uncertainty in wind and photovoltaic power forecasts, based on four different machine learning (ML) models. In our analysis, we found substantial differences in uncertainty depending on ML models, data coverage, and seasonal patterns that have to be considered in future planning studies. | en |
| dc.identifier.doi | 10.18420/inf2019_74 | |
| dc.identifier.isbn | 978-3-88579-688-6 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/25026 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-294 | |
| dc.subject | uncertainty analysis | |
| dc.subject | machine learning models | |
| dc.subject | seasonal effects | |
| dc.subject | data coverage | |
| dc.title | Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 598 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 585 | |
| gi.conference.date | 23.-26. September 2019 | |
| gi.conference.location | Kassel | |
| gi.conference.sessiontitle | Digitalisierung des Energiesystems |
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