Grouping of dynamic electricity consumption behaviour: An f ‐divergence based hierarchical clustering model

Under the digitalization trend in the energy sector, utilities are devoted to providing better service to their customers by mining knowledge in fine‐grained electricity consumption data. Understanding the group behaviour of customers by clustering method is essential to achieving this end. Different from shape‐based clustering methods, an f ‐divergence based hierarchical clustering model is proposed to group customers by their dynamic electricity consumption patterns. Modelling the electricity consumption by Markov chains, the customers’ consumption patterns are first summarized into transition probability matrixes. Then, dissimilarity measures based on f‐ divergence are calculated. Specifically, due to their superiority, squared Hellinger distance and total variation distance are used. The hierarchical clustering is then conducted based on the obtained distance matrixes. Using real‐world smart meter dataset, the proposed method is compared with other dynamic clustering candidates by using the revised silhouette score. And consumers’ dynamic consumption patterns are not only analysed from the global to local levels, but also the relationship between clustering results and external factors are delved into. The results show that the proposed method can produce highly representative clusters, and is able to provide insights on the implementation of the demand‐side management program.