Segmentation of Factories on Electricity Consumption Behaviors Using Load Profile Data

In recent years, the new achievements in the field of technology and data science allowed to gather detailed and well-structured information about electricity consumption behaviors of industrial enterprises. Such type of information can find numerous applications in the power distribution industry. The utilities often use the data from contracts to assign each industrial customer a class label according to this type defined in predetermined industry segmentation. Such type of fixed-chart segmentation is not able to satisfy the needs of modern enterprises for the flexible and dynamic determination of production modes. In this paper, we address this problem by proposing a new method for the segmentation of various types of factories based on their electricity consumption patterns represented in load profile data. It exploits the evolution-based characteristics of smart meter data of multiple types of factories to remove irrelevant features. We use data visualization to estimate the number of clusters and apply the well-known k-means algorithm on filtered data to generate segmentation. Experimental results on real load profile data collected with smart meters from manufacturing industries in Guangdong province of China have shown that the new clustering approach produced the meaningful segmentation of factories that reflect production operations.