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.

中文翻译：

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用于检测电动汽车充电站时间行为模式的矩阵方法

基于电动汽车（EV）的到达时间和与充电站的EV连接的持续时间，我们使用两种方法识别充电模式并得出具有相似充电模式的充电站组。基于规则的方法通过指定一组时间间隔和一个阈值来得出充电模式。在第二种方法中，我们将修改后的lp范数（作为矩阵相异性度量）与分层聚类相结合，并将其应用于自动识别充电模式和与该模式相关联的充电站组。在大型公共充电站网络中收集的数据集用于测试这两种方法。使用这两种方法，我们得出了收费模式。第一种基于规则的方法在推导预定义模式方面表现良好，而后者，