Wind 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.
Birincil Dil | İngilizce |
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Konular | Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer), Yazılım Testi, Doğrulama ve Validasyon |
Bölüm | Research Articles |
Yazarlar | |
Erken Görünüm Tarihi | 3 Haziran 2024 |
Yayımlanma Tarihi | |
Gönderilme Tarihi | 20 Nisan 2024 |
Kabul Tarihi | 20 Mayıs 2024 |
Yayımlandığı Sayı | Yıl 2024 Cilt: 5 Sayı: 1 |
This work is licensed under a Creative Commons Attribution 4.0 International License.