Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting

Shucheng Luo; Baoshi Wang; Qingzhong Gao; Y. Wang; Xinfu Pang

doi:10.1016/j.egyr.2024.08.078

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Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting

Shucheng Luo Baoshi Wang Qingzhong Gao Y. Wang Xinfu Pang

Year: 2024 Journal: Energy Reports Vol: 12 Pages: 2676-2689 Publisher: Elsevier BV

DOI: 10.1016/j.egyr.2024.08.078

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Keywords:

Computer science Term (time) Algorithm Stability (learning theory) Gradient descent Cluster analysis Artificial neural network Convolutional neural network Key (lock) Generalization Data mining Electric power system Artificial intelligence Machine learning Power (physics) Mathematics

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Energy Load and Power Forecasting

Physical Sciences → Engineering → Electrical and Electronic Engineering

Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting

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