LSTM Deep Learning Techniques for Wind Power Generation Forecasting

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dc.authorid0009-0003-0422-8352
dc.contributor.otherAhmed Babiker Abdalla, Ibrahim
dc.date.accessioned2025-01-08T09:48:35Z
dc.date.available2025-01-08T09:48:35Z
dc.date.issued15.06.2024
dc.departmentEnstitüler, Fen Bilimleri Enstitüsü, Yapay Zeka ve Veri Bilimi Ana Bilim Dalı (Disiplinlerarası)
dc.departmentMeslek Yüksekokulları, Sivas Teknik Bilimler Meslek Yüksekokulu
dc.description.abstractWind power generation forecasting is crucial for the optimal integration of renewable energy sources into power systems. Traditional forecasting methods often struggle to accurately predict wind energy production due to the complex and nonlinear relationships between wind speed, weather parameters, and power output. In recent years, deep learning techniques have emerged as promising alternatives for wind power forecasting. This conference paper provides a comprehensive review of deep learning techniques, with a specific focus on Long Short-Term Memory (LSTM) networks, for short-term wind power generation forecasting. Leveraging insights from recent research and empirical evaluations, this paper explores the effectiveness of LSTM networks in capturing temporal dependencies in wind data and improving prediction accuracy. The review highlights the potential of LSTM-based models to enhance the integration of wind energy into power systems and provides guidance for future research in this area.
dc.identifier.citationBabiker Abdalla Ibrahim, A., & Altun, K. (2024). LSTM Deep Learning Techniques for Wind Power Generation Forecasting. Journal of Soft Computing and Artificial Intelligence, 5(1), 41-47. https://doi.org/10.55195/jscai.1471257
dc.identifier.doihttps://doi.org/10.55195/jscai.1471257
dc.identifier.endpage47
dc.identifier.issue1
dc.identifier.startpage41
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30927
dc.identifier.volume5
dc.institutionauthorAltun, Kenan
dc.institutionauthorid0000-0001-7419-1901
dc.publisherJournal of Soft Computing and Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.titleLSTM Deep Learning Techniques for Wind Power Generation Forecasting

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