Abstract
Thousands of electric vehicles (EV), which are large in number and flexible in their use of electricity, will be connected to the power system in the near future, which will bring more uncertainty to the power system. Therefore, it is necessary to study the general characteristics of EV charging behaviours. In the charging process, big data regarding charging behaviour of EVs are generated. This paper proposes a big data mining technique based on Random Forest and Principle Component Analysis for EV charging behaviour to identify and analyse clusters with different charging characteristics from the big data. This paper uses Dundee’s January 2018 EV charging data to conduct experiments, and obtains the charging behaviour clusters of the workdays, weekends, and holidays of January. The superiority of the random forest algorithm in the EV clustering problem is reflected when compared to the Euclidean distance method. The clusters obtained by the random forest algorithm have clearer characteristics, including the user’s charging method and travel behaviour. The results show that the charging behaviour of EVs has certain regularity, and the charging load has obvious peak-to-valley difference that is necessary to be regulated.
Highlights
As environmental pressures and the lack of energy shortage become more serious, electric vehicles attract more and more attention because of their high energy efficiency and low emissions of pollutant gases [1]–[3]
The effectiveness and superiority of the Random Forest algorithm for the clustering of electric vehicles (EV) charging behaviour will be analysed
This paper proposed a big data mining technique based on Random Forest and Principle Component Analysis for electric vehicle charging behaviour to identify and analyse different types of charging behaviour characteristics
Summary
As environmental pressures and the lack of energy shortage become more serious, electric vehicles attract more and more attention because of their high energy efficiency and low emissions of pollutant gases [1]–[3]. Analysing the spatial and temporal distribution of EV charging load is the basis for studying the impact of large-scale development of electric vehicles on the power grid, capabilities of participating in grid interaction, and charge/discharge-control strategies [4]. The charging behaviours of EVs are normally random and diverse, which seemingly makes them complicated and hard to analyse [5]. Reference [7] proposed a calculation method that comprehensively considered the charging time distribution of different types of EVs, especially private cars, buses, and taxis.
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