Regional Wind Power Forecasting Based on CNNBiLSTM-ReliefF Method
Author:
Affiliation:
1. Tarbiat Modares University,Faculty of Interdisciplinary Science and Technology,Tehran,Iran
2. Tarbiat Modares University Tehran,Faculty of Electrical and Computer Engineering,Iran
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10607761/10607763/10607820.pdf?arnumber=10607820
Reference26 articles.
1. Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain
2. Global Wind Report 2023;Energy Council (GWEC),2023
3. A novel genetic LSTM model for wind power forecast
4. A novel wavenets long short term memory paradigm for wind power prediction
5. A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting
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