TY - GEN
T1 - Charging load forecasting of electric vehicle charging station based on support vector regression
AU - Sun, Qiming
AU - Liu, Jihong
AU - Rong, Xiaoxue
AU - Zhang, Meng
AU - Song, Xiangqian
AU - Bie, Zhaohong
AU - Ni, Zhaorui
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/9
Y1 - 2016/12/9
N2 - In allusion to the problem that electric vehicle(EV) charging time and state of charge(SOC) randomness leads to the traditional application of EV charging load characteristic forecasting method low accuracy problem, applying support vector regression(SVR), a charging load forecasting model based on historical load is proposed. The proposed model considers various kinds of factors which could influence the load, including the historical data of charging load, the number of EVs, the number of normal working charging pile, weather information, week properties, holiday properties and other information, in addition, the model corrects the false data before the establishment of the training sample set, which effectively improves the precision of forecasting. The effectiveness and correctness are validated by numerical example of an EV charging and switching station.
AB - In allusion to the problem that electric vehicle(EV) charging time and state of charge(SOC) randomness leads to the traditional application of EV charging load characteristic forecasting method low accuracy problem, applying support vector regression(SVR), a charging load forecasting model based on historical load is proposed. The proposed model considers various kinds of factors which could influence the load, including the historical data of charging load, the number of EVs, the number of normal working charging pile, weather information, week properties, holiday properties and other information, in addition, the model corrects the false data before the establishment of the training sample set, which effectively improves the precision of forecasting. The effectiveness and correctness are validated by numerical example of an EV charging and switching station.
KW - charging load forecasting
KW - electric vehicle
KW - state of charge (SOC)
KW - support vector regression(SVR)
UR - https://www.scopus.com/pages/publications/85009997033
U2 - 10.1109/APPEEC.2016.7779794
DO - 10.1109/APPEEC.2016.7779794
M3 - 会议稿件
AN - SCOPUS:85009997033
T3 - Asia-Pacific Power and Energy Engineering Conference, APPEEC
SP - 1777
EP - 1781
BT - IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference
PB - IEEE Computer Society
T2 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016
Y2 - 25 October 2016 through 28 October 2016
ER -