赵宇明,李玉璟,苏粟.多信息耦合下的充电站信息预测方法研究[J].电测与仪表,2023,60(9):57-64. Zhao Yuming,Li Yujing,Su Su.Research on information prediction method of charging station based on multiple information interconnections[J].Electrical Measurement & Instrumentation,2023,60(9):57-64.
多信息耦合下的充电站信息预测方法研究
Research on information prediction method of charging station based on multiple information interconnections
In order to solve the problem of "difficult charging stations" and charging time waiting for electric vehicle users when there is a demand for charging, this paper proposes a method for predicting the operating status of electric vehicle charging stations under the coupling of multiple information interconnections. We use the German map and e-charging network to collect Python traffic information and charging station information in the predicted area based on Python crawler technology, and analyze the correlation between the local weather conditions and the surrounding traffic conditions and the busy and idle status of the charging pile. A deep belief network prediction model is used to predict the use of charging piles in a charging station, and an example is used to analyze the actual data of a charging station. The results show that the proposed prediction model can more accurately predict the number of charging piles in a charging station. It is verified that the prediction results can provide a basis for the available charging stations for users in the short term in the future, and balance the equipment utilization rate between charging stations.
