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

Improving the accuracy of electric load forecasting is critical for grid stability, industrial production, and residents' daily lives. Traditional short-term load forecasting methods often struggle to fully capture the long-term dependencies and deep-seated features in unknown datasets, thus li...

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Veröffentlicht in:	Energy reports Jg. 12; S. 2676 - 2689
Hauptverfasser:	Luo, Shucheng, Wang, Baoshi, Gao, Qingzhong, Wang, Yibao, Pang, Xinfu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.12.2024
Schlagworte:	BiLSTM CNN Electricity load forecasting Stacking integration algorithm CNN BiLSTM Electricity load forecasting Stacking integration algorithm
ISSN:	2352-4847, 2352-4847
Online-Zugang:	Volltext
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