Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data

Nowadays, smart energy meters are being used to record periodic electricity consumption. The real time data produced by smart meters provide the detailed information about the electricity usage of a particular consumer. In this paper, we propose a motif-based association rule mining and clustering technique for determining the energy usage patterns for smart meter data. The association rules of motifs within a specific time window characterizes behaviors of energy consumer. In particular, we focus on an extraction of the temporal information of the smart meter. The process is based on the unique combination of Symbolic Aggregate approximation (SAX), temporal motif discovery and association rule mining to detect the expected and unexpected patterns robustly. Experiments on real world smart meter datasets justify that the proposed model discovers the useful routine behavior of electricity energy consumers, which are helpful for electricity utility experts. Further, in this paper, clustering on the motifs is performed which gives the different consumption behavior of consumers on different days which can help distribution network operator (DNO) for electricity network modeling and management. In future, we can form motif-based signature using the proposed approach for different applications such as anomaly detection and dynamic detection of operating patterns.