MATEC Web Conf.
Volume 176, 20182018 6th International Forum on Industrial Design (IFID 2018)
|Number of page(s)||5|
|Section||Intelligent Design and Computer Technology|
|Published online||02 July 2018|
Prediction of Operating Abnormality Rate of Charging Pile Based on Generalized AR(q) Combined Regression
NCEPRI(Huadian Electric Power Research Institute Co., Ltd.,) Beijing
Corresponding author :email@example.com
The stable operation of charging pile is related to the entire operation efficiency of the charging network of electric vehicles so the prediction of charging pile operation abnormality rate can help the operational department to make operational decisions in advance. This paper uses the electric vehicle charging network operating date in the north of Hebei province, based on the feature of the anomalies records of charging pile, to combine the generalized AR(q) model and the regression model and to predict the abnormality rate of electric vehicle charging network in the north of Hebei province. It is predicted that the average absolute error is 0.0044 and the acceptable prediction effect can be obtained.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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