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Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks

; ; ; ; ;

In
Energy and AI 17, Seiten/Artikel-Nr.:100401

ImpressumAmsterdam : Elsevier ScienceDirect

Umfang[1]-14

ISSN2666-5468

Online
DOI: 10.1016/j.egyai.2024.100401

DOI: 10.18154/RWTH-2024-12325
URL: https://publications.rwth-aachen.de/record/999757/files/999757.pdf

Einrichtungen

  1. Lehrstuhl für Energiesystemökonomik (817310)
  2. E.ON Energy Research Center (080052)


Thematische Einordnung (Klassifikation)
DDC: 624

OpenAccess:
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Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85200969452
WOS Core Collection: WOS:001295434900001

Interne Identnummern
RWTH-2024-12325
Datensatz-ID: 999757

Beteiligte Länder
Germany

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Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Article Processing Charges ; DOAJ Seal ; Fees ; SCOPUS
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080052
817310
 Record created 2024-12-23, last modified 2025-02-07
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