Short-term load forecasting based on wavelet decomposition and XGBoost

Ningning Zheng, Yunfeng Shao, Suli Zou, Zhongjing Ma*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The development of intelligent power systems and the large-scale access of distributed power sources have continuously deepened the impact on the power distribution side, and have placed higher requirements on the accuracy of load forecasting. In this paper, a short-term load forecasting method based on wavelet analysis and XGBoost is proposed. First, use wavelet analysis to classify power loads in different frequency bands, and then use the XGBoost model for training and prediction of the classified loads. Finally, using real power data in a certain area as a sample, the average absolute percentage error (MAPE) and the mean squared error(MSE) are used to compare and analyze the 24th hour data predicted by XGBoost and SVM and LSTM, respectively. The results show that XGBoost has a better fit and higher accuracy for short-term power loads.

Original languageEnglish
Title of host publicationWCSE 2020
Subtitle of host publication2020 10th International Workshop on Computer Science and Engineering
PublisherInternational Workshop on Computer Science and Engineering (WCSE)
Pages403-410
Number of pages8
ISBN (Electronic)9789811447877
DOIs
Publication statusPublished - 2020
Event2020 10th International Workshop on Computer Science and Engineering, WCSE 2020 - Shanghai, China
Duration: 19 Jun 202021 Jun 2020

Publication series

NameWCSE 2020: 2020 10th International Workshop on Computer Science and Engineering

Conference

Conference2020 10th International Workshop on Computer Science and Engineering, WCSE 2020
Country/TerritoryChina
CityShanghai
Period19/06/2021/06/20

Keywords

  • Load forecasting
  • Power system
  • Wavelet analysis
  • XGBoost

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Zheng, N., Shao, Y., Zou, S., & Ma, Z. (2020). Short-term load forecasting based on wavelet decomposition and XGBoost. In WCSE 2020: 2020 10th International Workshop on Computer Science and Engineering (pp. 403-410). (WCSE 2020: 2020 10th International Workshop on Computer Science and Engineering). International Workshop on Computer Science and Engineering (WCSE). https://doi.org/10.18178/wcse.2020.06.060