Deep Learning for Power System Applications: Case Studies Linking Artificial Intelligence and Power Systems
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing
2024
Cham Springer |
| Ausgabe: | 1st ed. 2024 |
| Schriftenreihe: | Power Electronics and Power Systems
|
| Schlagworte: | |
| Online-Zugang: | BFB01 BTU01 FAB01 FAN01 FAW01 FFW01 FHD01 FHI01 FHM01 FHN01 FHR01 FRO01 FWS01 FWS02 HTW01 TUM01 UBT01 UBY01 URL des Erstveröffentlichers |
| Beschreibung: | 1 Online-Ressource (XII, 101 p. 32 illus., 30 illus. in color) |
| ISBN: | 9783031453571 |
| ISSN: | 2196-3193 |
| DOI: | 10.1007/978-3-031-45357-1 |
Internformat
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| 650 | 4 | |a Electric power production | |
| 650 | 4 | |a Electric power distribution | |
| 650 | 4 | |a Energy policy | |
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| 650 | 4 | |a Artificial intelligence | |
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Datensatz im Suchindex
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| author | Li, Fangxing Du, Yan |
| author_facet | Li, Fangxing Du, Yan |
| author_role | aut aut |
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| author_variant | f l fl y d yd |
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| bvnumber | BV049441878 |
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| collection | ZDB-2-ENE |
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| dewey-full | 621.31 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.31 |
| dewey-search | 621.31 |
| dewey-sort | 3621.31 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1007/978-3-031-45357-1 |
| edition | 1st ed. 2024 |
| format | Electronic eBook |
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| id | DE-604.BV049441878 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:12:09Z |
| indexdate | 2025-02-20T06:40:36Z |
| institution | BVB |
| isbn | 9783031453571 |
| issn | 2196-3193 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034787844 |
| oclc_num | 1414541323 |
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| physical | 1 Online-Ressource (XII, 101 p. 32 illus., 30 illus. in color) |
| psigel | ZDB-2-ENE ZDB-2-ENE_2024 |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Springer International Publishing Springer |
| record_format | marc |
| series2 | Power Electronics and Power Systems |
| spellingShingle | Li, Fangxing Du, Yan Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems Electrical Power Engineering Energy Grids and Networks Energy Policy, Economics and Management Artificial Intelligence Machine Learning Electric power production Electric power distribution Energy policy Energy and state Artificial intelligence Machine learning |
| title | Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems |
| title_auth | Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems |
| title_exact_search | Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems |
| title_exact_search_txtP | Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems |
| title_full | Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems by Fangxing Li, Yan Du |
| title_fullStr | Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems by Fangxing Li, Yan Du |
| title_full_unstemmed | Deep Learning for Power System Applications Case Studies Linking Artificial Intelligence and Power Systems by Fangxing Li, Yan Du |
| title_short | Deep Learning for Power System Applications |
| title_sort | deep learning for power system applications case studies linking artificial intelligence and power systems |
| title_sub | Case Studies Linking Artificial Intelligence and Power Systems |
| topic | Electrical Power Engineering Energy Grids and Networks Energy Policy, Economics and Management Artificial Intelligence Machine Learning Electric power production Electric power distribution Energy policy Energy and state Artificial intelligence Machine learning |
| topic_facet | Electrical Power Engineering Energy Grids and Networks Energy Policy, Economics and Management Artificial Intelligence Machine Learning Electric power production Electric power distribution Energy policy Energy and state Artificial intelligence Machine learning |
| url | https://doi.org/10.1007/978-3-031-45357-1 |
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