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 |
|---|---|
| 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 |
|---|---|
| Band | 10097 |
| ISSN | 0302-9743 |
Konferenz
| Titel | 4th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2016 |
|---|---|
| Dauer | 23 September 2016 |
| Stadt | Riva del Garda |
| Land | Italien |
Externe IDs
| ORCID | /0000-0001-8107-2775/work/142253529 |
|---|
Schlagworte
Ziele für nachhaltige Entwicklung
- SDG 7 – Erschwingliche und saubere Energie - Machine learning, Practical relevance, Renewable energy forecasting
ASJC Scopus Sachgebiete
- Theoretische Informatik - Machine learning, Practical relevance, Renewable energy forecasting
- Allgemeine Computerwissenschaft - Machine learning, Practical relevance, Renewable energy forecasting
Schlagwörter
- Machine learning, Practical relevance, Renewable energy forecasting