Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD

Accurate long-term power load forecasting in the grid is crucial for supply–demand balance analysis in new power systems. It helps to identify potential power market risks and uncertainties in advance, thereby enhancing the stability and efficiency of power systems. Given the temporal and nonlinear...

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Principais autores:	Xiaoyu Liu, Jiangfeng Song, Hai Tao, Peng Wang, Haihua Mo, Wenjie Du
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2025-05-01
Series:	Energies
Assuntos:	long-term load forecasting CNN-BiLSTM attention mechanism CEEMDAN K-means clustering VMD
Acceso en liña:	https://www.mdpi.com/1996-1073/18/11/2675
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