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

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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Bibliographic Details
Main Authors: Xiaoyu Liu, Jiangfeng Song, Hai Tao, Peng Wang, Haihua Mo, Wenjie Du
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Energies
Subjects:
long-term load forecasting
CNN-BiLSTM
attention mechanism
CEEMDAN
K-means clustering
VMD
Online Access:https://www.mdpi.com/1996-1073/18/11/2675
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