Deep learning for variable renewable energy: a systematic review
- In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.
| Author of HS Reutlingen | van Dinther, Clemens |
|---|---|
| DOI: | https://doi.org/10.1145/3586006 |
| ISSN: | 0360-0300 |
| Erschienen in: | ACM Computing Surveys |
| Publisher: | Association for Computing Machinery |
| Place of publication: | New York |
| Document Type: | Journal article |
| Language: | English |
| Publication year: | 2023 |
| Tag: | advance forecasts; artificial intelligence; computing methodologies; deep learning; enhance condition monitoring; machine learning; optimize power system scheduling; renewable energy generation; solar pv; wind power |
| Volume: | 56 |
| Issue: | 1 |
| Page Number: | 37 |
| First Page: | 1 |
| Last Page: | 37 |
| DDC classes: | 004 Informatik |
| Open access?: | Nein |
| Licence (German): |  In Copyright - Urheberrechtlich geschützt |
