Electric vehicle charging load prediction method considering data correlation

Abstract

The invention relates to an electric vehicle charging load prediction method considering data correlation. The electric vehicle charging load prediction method comprises the following steps: step 1, collecting electric vehicle charging load historical data; step 2, performing data correlation analysis on the historical data and the real-time data of the charging load of the electric vehicle acquired in the step 1, and calculating a correlation coefficient between the historical data and the real-time data of the charging load of the electric vehicle; step 3, based on the correlation coefficient obtained through calculation in the step 2, selecting electric vehicle charging load historical data with high correlation as predicted electric vehicle charging load data; and step 4, taking the historical data of the electric vehicle charging load with high correlation selected in the step 3 as predicted electric vehicle charging load data, and performing prediction by adopting an LSTM algorithm to obtain a prediction result. The method can effectively reduce the workload of data processing, simplifies the prediction method, and improves the prediction precision.

Description

technical field [0001] The invention belongs to the technical field of electric vehicle load data analysis, and relates to a method for predicting the charging load of electric vehicles, in particular to a method for predicting the charging load of electric vehicles considering data correlation. Background technique [0002] With the increasingly prominent energy and environmental issues, in order to implement the national energy development strategy and build a clean, efficient, safe and sustainable modern energy system, electric vehicles have been vigorously developed. Among public service vehicles from 2018 to 2020, the number of newly added electric vehicles has increased to 30% to 50% each year. On March 20, at the "New Revolution in the Automobile Industry" sub-forum of the 2021 China Development Forum, Zhang Yongwei, vice chairman and secretary-general of the China Electric Vehicle Association, said that according to forecasts, around 2030, the number of electric vehi...

AI Technical Summary

Problems solved by technology

However, the existing electric vehicle charging load forecasting methods have the disadvantages of difficult forecasting and low forecasting reliability.

Images