• Login
    View Item 
    •   DSpace Home
    • 6-ULUSAL ve ULUSLARARASI İNDEKSLERDE B.E.Ü. YAYINLARI
    • 01) WOS İndeksli Yayınlar Koleksiyonu
    • View Item
    •   DSpace Home
    • 6-ULUSAL ve ULUSLARARASI İNDEKSLERDE B.E.Ü. YAYINLARI
    • 01) WOS İndeksli Yayınlar Koleksiyonu
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    An improved residual-based convolutional neural network for very short-term wind power forecasting

    Thumbnail
    Date
    2021
    Author
    Yildiz, Ceyhun
    Acikgoz, Hakan
    Korkmaz, Deniz
    Budak, Umit
    Metadata
    Show full item record
    Abstract
    An accurate forecast of wind power is very important in terms of economic dispatch and the operation of power systems. However, effectively mitigating the risks arising from wind power in power system operations greatly reduces the risk of wind energy pro
    URI
    http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/9874
    https://doi.org/10.1016/j.enconman.2020.113731
    Collections
    • 01) WOS İndeksli Yayınlar Koleksiyonu [1011]
    • 02) Scopus İndeksli Yayınlar Koleksiyonu [895]





    Creative Commons License
    DSpace@BEU by Bitlis Eren University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     




    | Yönerge | Rehber | İletişim |

    sherpa/romeo

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV