The Prediction of Electric Vehicle Charging Load

DOI

10.2991/icmcs-18.2018.116How to use a DOI?

Keywords

Electric Vehicle; Load Forecasting; Optimization Algorithm; Driving Rule; Charging Mode

Abstract

Because electric vehicle charging has the characteristics of strong randomness and unpredictability, it will inevitably have a certain impact on the power system. To effectively predict the charging load of electric vehicles can effectively alleviate the impact of electric vehicle charging on the distribution network to a certain extent. An electric vehicle charging load forecasting method using neural network and genetic algorithm is proposed in this paper. This method fully considers the influence of driving rule, charging characteristics, seasonal change, road condition and other factors. Relevant experimental data show that the prediction method has good prediction accuracy.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Song Teng
PY  - 2018/10
DA  - 2018/10
TI  - The Prediction of Electric Vehicle Charging Load
BT  - Proceedings of the 8th International Conference on Management and Computer Science (ICMCS 2018)
PB  - Atlantis Press
SP  - 565
EP  - 568
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmcs-18.2018.116
DO  - 10.2991/icmcs-18.2018.116
ID  - Teng2018/10
ER  -