TY - GEN
T1 - Short-term load forecasting for the electric bus station based on GRA-DE-SVR
AU - Xiaobo, Xu
AU - Liu, Wenxia
AU - Xi, Zhou
AU - Tianyang, Zhao
PY - 2014
Y1 - 2014
N2 - With large-scale electric vehicles penetrating into power system, the grid will be faced with severe challenges. Accurate charging load forecasting is required to ensure the security and economy of the grid. Firstly, the factors that influence the daily load of electric bus stations are analyzed in this paper. Based on the grey relation theory, samples of similar days are selected to establish SVM prediction model. In order to improve prediction accuracy, differential evolution (DE) algorithm is applied to optimize parameters of SVR model. Through empirical study, the root mean square error (RMSE) of daily load forecasting is 10.85%. Compared with the standard SVM prediction model, the prediction precision of this paper is increased by 1.52%. What's more, the proposed method has better forecasting performance than the other methods.
AB - With large-scale electric vehicles penetrating into power system, the grid will be faced with severe challenges. Accurate charging load forecasting is required to ensure the security and economy of the grid. Firstly, the factors that influence the daily load of electric bus stations are analyzed in this paper. Based on the grey relation theory, samples of similar days are selected to establish SVM prediction model. In order to improve prediction accuracy, differential evolution (DE) algorithm is applied to optimize parameters of SVR model. Through empirical study, the root mean square error (RMSE) of daily load forecasting is 10.85%. Compared with the standard SVM prediction model, the prediction precision of this paper is increased by 1.52%. What's more, the proposed method has better forecasting performance than the other methods.
KW - Electric vehicles
KW - differential evolution
KW - grey relation analysis
KW - short-term load forecasting
KW - support vector machine
UR - https://www.scopus.com/pages/publications/84906691067
U2 - 10.1109/ISGT-Asia.2014.6873823
DO - 10.1109/ISGT-Asia.2014.6873823
M3 - 会议稿件
AN - SCOPUS:84906691067
SN - 9781479913008
T3 - 2014 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2014
SP - 388
EP - 393
BT - 2014 IEEE Innovative Smart Grid Technologies - Asia, ISGT ASIA 2014
PB - IEEE Computer Society
T2 - 2014 IEEE Innovative Smart Grid Technologies - Asia, ISGT Asia 2014
Y2 - 20 May 2014 through 23 May 2014
ER -