Poster Abstract: Appliance Classification and Energy Management Using Multi-Modal Sensing
In this demonstration, we introduce a low-cost energy management system that tracks appliance energy usage and identiﬁes particular sources of waste that can be optimized. In order to better understand appliance usage patterns, we correlate electrical load information with environmental sensors to identify clusters. These patterns can be used to identify when devices are accidentally left active in unoccupied rooms and provide a means to identify excessive consumption. The correlation is based on learned information over time and hence requires minimal manual labeling. Our system combines measurements from a circuit-panel energy
meter with multiple low-cost wireless sensors. We utilize an EMF-based appliance state detector that when combined with circuit-panel and plug-load energy meters allows the system to track the energy consumption of loads at a lower cost and in a less invasive manner than previous metering systems. We deployed our system in a house, collecting data from over 60 sensing points for more than six months. During this period, the system was able to identify wasteful energy usage as high as 17% of the total daily consumption.