Dealing with uncertainty: An empirical study on the relevance of renewable energy forecasting methods
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Beitragende
Abstract
The increasing share of fluctuating renewable energy sources on the world-wide energy production leads to a rising public interest in dedicated forecasting methods. As different scientific communities are dedicated to that topic, many solutions are proposed but not all are suited for users from utility companies. We describe an empirical approach to analyze the scientific relevance of renewable energy forecasting methods in literature. Then, we conduct a survey amongst forecasting software providers and users from the energy domain and compare the outcomes of both studies.
Details
Originalsprache | Englisch |
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Titel | Data Analytics for Renewable Energy Integration |
Redakteure/-innen | Zeyar Aung, Stuart Madnick, Oliver Kramer, Wei Lee Woon |
Herausgeber (Verlag) | Springer, Berlin [u. a.] |
Seiten | 54-66 |
Seitenumfang | 13 |
ISBN (Print) | 9783319509464 |
Publikationsstatus | Veröffentlicht - 2017 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | Lecture Notes in Computer Science, Volume 10097 |
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ISSN | 0302-9743 |
Konferenz
Titel | 4th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2016 |
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Dauer | 23 September 2016 |
Stadt | Riva del Garda |
Land | Italien |
Externe IDs
ORCID | /0000-0001-8107-2775/work/142253529 |
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Schlagworte
Ziele für nachhaltige Entwicklung
ASJC Scopus Sachgebiete
Schlagwörter
- Machine learning, Practical relevance, Renewable energy forecasting