Short Term Prediction of Electric Vehicle Charging Load Based on Optimized Genetic Algorithm
- https://doi.org/10.2991/meici-18.2018.123How to use a DOI?
- Electric vehicle; Load forecasting; Genetic algorithm; BP neural network
With the continuous attention and promotion of electric vehicles, governments of electric vehicles have made great progress. However, due to the randomness and unpredictable nature of electric vehicle charging, it will 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. This paper proposes a method to predict the charging load of electric vehicles by using the genetic algorithm to optimize the numerical value and weight threshold of the number of the hidden layer units of the neural network structure, and compares it with the BP neural network prediction method. The experimental data show that the prediction method has higher prediction accuracy.
- © 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/).
Cite this article
TY - CONF AU - Tianyi Qu PY - 2018/12 DA - 2018/12 TI - Short Term Prediction of Electric Vehicle Charging Load Based on Optimized Genetic Algorithm BT - Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018) PB - Atlantis Press SP - 625 EP - 627 SN - 1951-6851 UR - https://doi.org/10.2991/meici-18.2018.123 DO - https://doi.org/10.2991/meici-18.2018.123 ID - Qu2018/12 ER -