Open AccessCharging Demand Forecasting Method Based on Historical Data
Author(s) -
Shi Shuanglong,
Zhe Yan,
Shuaihua Li,
Meng Da,
Xing Yuheng,
Hui Xie,
Wu Chunyan
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/295/3/032002
Subject(s) - database transaction , computer science , demand forecasting , electricity demand , entertainment , work (physics) , electric vehicle , transaction data , on demand , operations research , engineering , database , mechanical engineering , art , power (physics) , multimedia , physics , electricity generation , quantum mechanics , visual arts
This paper discusses a method for predicting the demand for charging using transaction data combined with data on the growth of the number of electric vehicles. The prediction result of charging demand can be used as an important reference for charging pile planning. The needs of electric vehicles can be divided into three different scenarios, night scenes, work scenes and entertainment scenes. In each scenario, we used Clark’s negative exponential equation to describe the distribution of charging demand in space.