A matrix approach to detect temporal behavioral patterns at electric vehicle charging stations

TL;DR

This paper introduces a matrix-based method to identify and analyze temporal charging patterns at electric vehicle stations, using rule-based and clustering approaches on real-world data.

Contribution

It proposes a novel matrix approach combining rule-based and hierarchical clustering methods to automatically detect charging patterns at EV stations.

Findings

Rule-based approach effectively identifies predefined patterns.

Hierarchical clustering reveals unexpected charging behaviors.

Both methods successfully analyze large EV charging datasets.

Abstract

Based on the electric vehicle (EV) arrival times and the duration of EV connection to the charging station, we identify charging patterns and derive groups of charging stations with similar charging patterns applying two approaches. The ruled based approach derives the charging patterns by specifying a set of time intervals and a threshold value. In the second approach, we combine the modified l-p norm (as a matrix dissimilarity measure) with hierarchical clustering and apply them to automatically identify charging patterns and groups of charging stations associated with such patterns. A dataset collected in a large network of public charging stations is used to test both approaches. Using both methods, we derived charging patterns. The first, rule-based approach, performed well at deriving predefined patterns and the latter, hierarchical clustering, showed the capability of delivering unexpected charging patterns.