PowerSAX: Fast motif matching in distributed power meter data using symbolic representations

Distributed power meters (also termed smart plugs) are embedded systems that measure the electric power consumption of individual appliances at a fine temporal resolution. They enable a wide range of novel smart services, e.g., accurately forecasting power consumption or making recommendations how to save energy. However, distributed power metering combines high sampling rates with a potentially large number of monitored outlets. A torrent of power readings may thus be generated, incurring high bandwidth requirements for their transmission and a significant computational power demand for their processing. In this paper, we present a concept for the efficient local storage and processing of power consumption data called PowerSAX. Instead of operating on raw sensor readings, PowerSAX converts consumption data into their symbolic representations and thus mitigates their storage requirement. Subsequently, it enables embedded systems to recognize relevant patterns (motifs) in the symbolic representations of collected data. By only transmitting a message when a known motif is encountered in the sensor data, PowerSAX can significantly reduce an application´s bandwidth requirements. We evaluate PowerSAX using real-world power consumption data and show to which extent smart plugs can make predictions of an appliance´s future power demand.