@inproceedings{d3a7c162177a4eb8808024780a9ef544,
title = "Probabilistic forecasting model for non-normally distributed EV charging demand",
abstract = "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.",
keywords = "Demand forecasting, Electric vehicle, Ensemble forecasting, Prediction interval, Probabilistic forecasting",
author = "Daisuke Kodaira and Junji Kondoh",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE; 2020 International Conference on Smart Grids and Energy Systems, SGES 2020 ; Conference date: 23-11-2020 Through 26-11-2020",
year = "2020",
month = nov,
doi = "10.1109/SGES51519.2020.00116",
language = "English",
series = "Proceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "623--626",
booktitle = "Proceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020",
}