Name
Abstract | ||
|---|---|---|
Large scale of electric vehicles (EVS) integration will pose great impacts on the power system, due to their disorderly charging. Electric cars' charging load cannot be forecasted as the traditional power load, which is usually forecasted based on historical data. There need to be some other methods to predict electric vehicles charging load, in order to improve the reliability and security of the grid. This paper analyze the travel characteristics of electric vehicles, then use the fuzzy inference system to emulate the process of drivers' decision to charge their cars, the charging probability is attained in the given location. Finally, the daily profile of charging load can be predicted according to the numbers of electric vehicles forecasted in Beijing. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-45643-9_62 | Communications in Computer and Information Science |
Keywords | Field | DocType |
electric vehicle,travel characteristics,charging load,fuzzy inference | Automotive engineering,Electric vehicle,Computer science,Fuzzy inference,Electric power system,Load forecasting,Electric cars,Artificial intelligence,Grid,Machine learning,Beijing,Fuzzy inference system | Conference |
Volume | ISSN | Citations |
484 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingwei Yang | 1 | 0 | 0.34 |
| Diansheng Luo | 2 | 0 | 0.34 |
| Shuang Yang | 3 | 0 | 0.34 |
| Shiyu Hu | 4 | 0 | 0.34 |