TY - GEN
T1 - Short-term load forecasting based on wavelet decomposition and XGBoost
AU - Zheng, Ningning
AU - Shao, Yunfeng
AU - Zou, Suli
AU - Ma, Zhongjing
N1 - Publisher Copyright:
© WCSE 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Load forecasting
KW - Power system
KW - Wavelet analysis
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85092366790&partnerID=8YFLogxK
U2 - 10.18178/wcse.2020.06.060
DO - 10.18178/wcse.2020.06.060
M3 - Conference contribution
AN - SCOPUS:85092366790
T3 - WCSE 2020: 2020 10th International Workshop on Computer Science and Engineering
SP - 403
EP - 410
BT - WCSE 2020
PB - International Workshop on Computer Science and Engineering (WCSE)
T2 - 2020 10th International Workshop on Computer Science and Engineering, WCSE 2020
Y2 - 19 June 2020 through 21 June 2020
ER -