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Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection : A Call to Action

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007598
URN to cite this document: urn:nbn:de:bvb:703-epub-7598-2

Title data

Principato, Marc ; Hasselwander, Lisa ; Stangner, Michael ; Büttner, Ricardo:
Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection : A Call to Action.
In: IEEE Access. Vol. 11 (2023) . - pp. 64026-64048.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3287861

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Further data

Item Type: Article in a journal
Keywords: Energy management; Wind turbines; Wind energy; Machine learning; Renewable energy sources; Meteorology; Ecosystems; Machine learning; Energy conservation; Energy transition; environmental conservation; wind energy; machine learning
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science > Chair Business Administration XVIII - Information Systems Management and Data Science - Univ.-Prof. Dr. Ricardo Büttner
Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7598-2
Date Deposited: 19 Mar 2024 10:20
Last Modified: 19 Mar 2024 10:21
URI: https://epub.uni-bayreuth.de/id/eprint/7598

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