Probabilistic Forecasting Model for Non-normally Distributed EV Charging Demand

被引：6
|
作者：
Kodaira, Daisuke [1 ]
Kondoh, Junji [1 ]
机构：
[1] Tokyo Univ Sci, Dept Elect Engn, Chiba, Japan
来源：
2020 INTERNATIONAL CONFERENCE ON SMART GRIDS AND ENERGY SYSTEMS (SGES 2020) | 2020年
关键词：
probabilistic forecasting; prediction interval; ensemble forecasting; electric vehicle; demand forecasting; ELECTRIC VEHICLES; REDUCTION; LOAD; STORAGE;
D O I：
10.1109/SGES51519.2020.00116
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
A method for probabilistic electric vehicle (EV) demand forecasting is proposed in this paper. The EV demand in a certain area is forecasted by an ensemble forecasting model. The forecast result includes a deterministic forecasting and prediction interval that indicates the probability of deviation from deterministic forecasting. In the case study, an actual observed dataset from the UK is used to verify the proposed algorithm. The results show that the target EV charging demand to be forecasted shows 80% prediction interval coverage for 27 days out of 30 simulated days.
引用
收藏
页码：623 / 626
页数：4
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