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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| Abstract | 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 limiting their generalization ability. In this paper, we propose an algorithm for short-term power load forecasting based on the stacking integration algorithm of Convolutional Neural Network-Bidirectional Long Short-Term Neural Network-Attention Mechanism (CNN-BiLSTM-Attention) with Extreme Gradient Tree (XGBoost). First, an adaptive hierarchical clustering algorithm (AHC) selects a dataset with similar day characteristics. Then, combined with influencing factors, the Stacking integrated algorithm based on CNN-BiLSTM-Attention and XGBoost is employed for forecasting short-term load data. Finally, the integrated algorithm model was applied to the multi-feature load dataset in the Quanzhou area from 2016 to 2018. Comparative analysis showed that MAPE could be reduced by 5.88–69.40 % in the four selected typical days compared to the comparative algorithm, significantly improving load forecasting accuracy.
•AHC is used to select similar daily samples.•Presented a Stacking ensemble algorithm based on CNN-BiLSTM-Attention and XGBoost.•Designed and conducted comparison experiments.•Conducted sensitivity analysis experiments. |
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| AbstractList | 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 limiting their generalization ability. In this paper, we propose an algorithm for short-term power load forecasting based on the stacking integration algorithm of Convolutional Neural Network-Bidirectional Long Short-Term Neural Network-Attention Mechanism (CNN-BiLSTM-Attention) with Extreme Gradient Tree (XGBoost). First, an adaptive hierarchical clustering algorithm (AHC) selects a dataset with similar day characteristics. Then, combined with influencing factors, the Stacking integrated algorithm based on CNN-BiLSTM-Attention and XGBoost is employed for forecasting short-term load data. Finally, the integrated algorithm model was applied to the multi-feature load dataset in the Quanzhou area from 2016 to 2018. Comparative analysis showed that MAPE could be reduced by 5.88–69.40 % in the four selected typical days compared to the comparative algorithm, significantly improving load forecasting accuracy.
•AHC is used to select similar daily samples.•Presented a Stacking ensemble algorithm based on CNN-BiLSTM-Attention and XGBoost.•Designed and conducted comparison experiments.•Conducted sensitivity analysis experiments. |
| Author | Luo, Shucheng Wang, Yibao Pang, Xinfu Gao, Qingzhong Wang, Baoshi |
| Author_xml | – sequence: 1 givenname: Shucheng surname: Luo fullname: Luo, Shucheng organization: Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China – sequence: 2 givenname: Baoshi surname: Wang fullname: Wang, Baoshi email: wangbs@sie.edu.cn organization: Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China – sequence: 3 givenname: Qingzhong surname: Gao fullname: Gao, Qingzhong organization: Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China – sequence: 4 givenname: Yibao surname: Wang fullname: Wang, Yibao organization: Zhangjiakou Electric Power Supply Company, State Grid Jibei Electric Power Company Limited, Zhangjiakou 075000, China – sequence: 5 givenname: Xinfu surname: Pang fullname: Pang, Xinfu organization: Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China |
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| Keywords | CNN BiLSTM Electricity load forecasting Stacking integration algorithm |
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