Machine learning and computer vision for renewable energy:
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey PA, USA
IGI Global
[2024]
|
| Schriftenreihe: | Advances in environmental engineering and green technologies (AEEGT) book series
|
| Schlagworte: | |
| Online-Zugang: | DE-898 DE-91 DE-1050 URL des Erstveröffentlichers |
| Zusammenfassung: | As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine learning and computer vision for renewable energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. |
| Beschreibung: | 1 Online-Ressource (xviii, 333 Seiten) Illustrationen |
| ISBN: | 9798369323564 |
| DOI: | 10.4018/979-8-3693-2355-7 |
Internformat
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Datensatz im Suchindex
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| discipline | Wirtschaftswissenschaften |
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| format | Electronic eBook |
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| id | DE-604.BV049735937 |
| illustrated | Illustrated |
| indexdate | 2024-12-17T19:01:33Z |
| institution | BVB |
| isbn | 9798369323564 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035078046 |
| oclc_num | 1443589842 |
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| physical | 1 Online-Ressource (xviii, 333 Seiten) Illustrationen |
| psigel | ZDB-98-IGB ZDB-98-IGB FHR_PDA_IGB ZDB-98-IGB TUM_Paketkauf_2024 |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | IGI Global |
| record_format | marc |
| series2 | Advances in environmental engineering and green technologies (AEEGT) book series |
| spelling | Machine learning and computer vision for renewable energy Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman Hershey PA, USA IGI Global [2024] © 2024 1 Online-Ressource (xviii, 333 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Advances in environmental engineering and green technologies (AEEGT) book series As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine learning and computer vision for renewable energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Renewable energy sources Technological innovations Acharjya, Pinaki Pratim edt Koley, Santanu 1981- (DE-588)1309260834 edt Barman, Subhabrata 1974- (DE-588)1309261067 edt Erscheint auch als Druck-Ausgabe 979-8-3693-2355-7 Erscheint auch als Druck-Ausgabe, Paperback 979-8-3693-4704-1 https://doi.org/10.4018/979-8-3693-2355-7 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Machine learning and computer vision for renewable energy Renewable energy sources Technological innovations |
| title | Machine learning and computer vision for renewable energy |
| title_auth | Machine learning and computer vision for renewable energy |
| title_exact_search | Machine learning and computer vision for renewable energy |
| title_full | Machine learning and computer vision for renewable energy Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman |
| title_fullStr | Machine learning and computer vision for renewable energy Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman |
| title_full_unstemmed | Machine learning and computer vision for renewable energy Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman |
| title_short | Machine learning and computer vision for renewable energy |
| title_sort | machine learning and computer vision for renewable energy |
| topic | Renewable energy sources Technological innovations |
| topic_facet | Renewable energy sources Technological innovations |
| url | https://doi.org/10.4018/979-8-3693-2355-7 |
| work_keys_str_mv | AT acharjyapinakipratim machinelearningandcomputervisionforrenewableenergy AT koleysantanu machinelearningandcomputervisionforrenewableenergy AT barmansubhabrata machinelearningandcomputervisionforrenewableenergy |