Ultra Short Term Load Power Prediction Considering Feature Recombination and BiGRU-Attention-XGBoost Model

LI Lianbing; GAO Guoqiang; CHEN Weiguang; FU Wenjie; ZHANG Chao; ZHAO Shasha

doi:10.19725/j.cnki.1007-2322.2023.0166

Modern Electric Power > 2023 > Accepted Manuscript > DOI: 10.19725/j.cnki.1007-2322.2023.0166

LI Lianbing, GAO Guoqiang, CHEN Weiguang, et al. Ultra Short Term Load Power Prediction Considering Feature Recombination and BiGRU-Attention-XGBoost Model[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2023.0166

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Ultra Short Term Load Power Prediction Considering Feature Recombination and BiGRU-Attention-XGBoost Model

1.
State Key Laboratory of Electrical Equipment Reliability and Intelligence(Hebei University of Technology), Hongqiao District, Tianjin 300131, China
2.
School of Artificial Intelligence and Data Science, Hebei University of Technology, Hongqiao District, Tianjin 300131, China
3.
State Grid Hebei Electric Power Co., Ltd, Shijiazhuang 050000, Hebei Province, China
4.
State Grid Hebei Electric Power Co., Ltd. Marketing Service Center, Shijiazhuang 050000, Hebei Province, China

Funds: Project Supported by Science and Technology Program of Hebei Province(20314301D).

More Information

Received Date: May 07, 2023
Accepted Date: November 29, 2023
Available Online: December 24, 2023

Graphical Abstract

Abstract

Abstract

The ultra-short-term load forecasting, as a basic component of power system forecasting, is of great significance for production scheduling planning. However, the power load, being nonlinear, time-varying and uncertain in nature, necessitates the improvement of to prediction accuracy by fully exploiting its potential characteristics and conducting separate prediction. In this paper, a combined forecasting model of BiGRU-Attention-XGBoost based on ALIF-SE is proposed. The historical load series are decomposed into periodic series, fluctuation series and trend series based on ALIF-SE. An attention mechanism is employed to improve the BIGRU model. It is then combined with XGBoost model to construct a power load forecasting model based on time-varying weight combination. The experimental results demonstrate a significant improvement in prediction accuracy of the input model after ALIF-SE treatment. Moreover, the combined model exhibits good prediction effect for both working days and holidays, with the majority of the prediction errors below 5%. The mobility of the proposed method is verified by comparing the results of experiments with different load data sets. The experimental results demonstrate the effectiveness, accuracy, and feasibility of the proposed model.
- adaptive local iterative filtering(ALIF),
- sample entropy(SE),
- deep learning,
- combination model,
- ultra-short-term load forecasting

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References (24)

References

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