A Comprehensive Approach to Wind Power Forecasting Using Advanced Hybrid Neural Networks

Wind power prediction is important in successfully integrating renewable energy sources into the grid. This study is focused on a sub-domain of wind power prediction and compares Bidirectional Long Short Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) architectures. Additionally,...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.124790-124800
Hauptverfasser:	Vishnutheerth, E. P., Vijay, Vivek, Satheesh, Rahul, Kolhe, Mohan Lal
Format:	Artikel
Sprache:	eng
Schlagworte:	Alternative energy sources Artificial neural networks bidirectional gated recurrent unit (BiGRU) Bidirectional long short term memory (BiLSTM) BiLSTM Convolutional neural networks convolutional neural networks (CNN) Data models Deep learning Discrete Wavelet Transform Discrete Wavelet Transform (DWT) Discrete wavelet transforms Energy management Error analysis Feature extraction hybrid deep learning Long short term memory Machine learning Neural networks Performance indices Predictive models Recurrent neural networks Reliability Renewable energy sources Root-mean-square errors Wavelet transforms Wind power Wind power generation
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