Enhancing wind power forecasting using novel LSTM-dynamic context-aware power forecasting hybrid model.

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Title: Enhancing wind power forecasting using novel LSTM-dynamic context-aware power forecasting hybrid model.
Authors: Jan, Yasir1,2, Jie, Yan1, Sydney, Mutale1,3, sydney.mutale@unza.zm, Ahmed, Saeed4, KC, Ishwor1
Source: Journal of Algorithms & Computational Technology; 2/27/2026, Vol. 20, p1-16, 16p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 191983975
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=191983975
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1177/17483026261428634
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 16
        StartPage: 1
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      – TitleFull: Enhancing wind power forecasting using novel LSTM-dynamic context-aware power forecasting hybrid model.
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            NameFull: Jan, Yasir
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            NameFull: Jie, Yan
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            NameFull: Sydney, Mutale
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            NameFull: Ahmed, Saeed
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            – D: 27
              M: 02
              Text: 2/27/2026
              Type: published
              Y: 2026
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              Value: 20
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