Enhancing Wind Power Forecasting Accuracy Based on OPESC-Optimized CNN-BiLSTM-SA Model

Enhancing Wind Power Forecasting Accuracy Based on OPESC-Optimized CNN-BiLSTM-SA Model

Accurate wind power forecasting is critical for grid management and low-carbon transitions, yet challenges arise from wind dynamics’ nonlinearity and randomness. Existing methods face issues like suboptimal hyperparameters and a poor spatiotemporal feature integration. This study proposes OPESC-CNN-...

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Bibliographic Details
Main Authors:	Lele Wang, Dongqing Zhang
Format:	Article
Language:	English
Published:	MDPI AG 2025-07-01
Series:	Mathematics
Subjects:	wind power forecasting escape algorithm (ESC) convolutional neural network (CNN) bidirectional long short-term memory network (BiLSTM) self-attention mechanism
Online Access:	https://www.mdpi.com/2227-7390/13/13/2174
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