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10 August 2023 Multi-feature short-term load forecasting based on stacking ensemble learning
Xin He, Chengbo Yu, Shibin Wang, Wei Zhang, Jia Chen
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Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274826 (2023) https://doi.org/10.1117/12.2689388
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China
Abstract
Short-term power load forecasting plays an important role in power system dispatching. To improve forecasting accuracy, a short-term load forecasting model based on stacking ensemble learning was proposed. Firstly, add effective multi-feature variables, and establishes a Stacking ensemble learning model for the load data and feature, which was ensembles by Light Gradient Boosting Machine (abbr. LightGBM) and eXtreme Gradient Boosting (abbr. XGBoost) for prediction. Finally, the comparison and experimental results show that the forecasting error of the proposed model is less than that of the comparative model.
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Xin He, Chengbo Yu, Shibin Wang, Wei Zhang, and Jia Chen "Multi-feature short-term load forecasting based on stacking ensemble learning", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274826 (10 August 2023); https://doi.org/10.1117/12.2689388
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KEYWORDS
Data modeling
Deep learning
Power grids
Time series analysis
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