Combining Smart Speaker and Smart Meter to Infer Your Residential Power Usage by Self-supervised Cross-modal Learning

Abstract

Energy disaggregation is a key enabling technology for residential power usage monitoring, which benefits various applications such as carbon emission monitoring and human activity recognition. However, existing methods are difficult to balance the accuracy and usage burden (device costs, data labeling and prior knowledge). As the high penetration of smart speakers offers a low-cost way for sound-assisted residential power usage monitoring, this work aims to combine a smart speaker and a smart meter in a house to liberate the system from a high usage burden. However, it is still challenging to extract and leverage the consistent/complementary information (two types of relationships between acoustic and power features) from acoustic and power data without data labeling or prior knowledge. To this end, we design COMFORT, a cross-modality system for self-supervised power usage monitoring, including (i) a cross-modality learning component to automatically learn the consistent and complementary information, and (ii) a cross-modality inference component to utilize the consistent and complementary information. We implement and evaluate COMFORT with a self-collected dataset from six houses in 14 days, demonstrating that COMFORT finds the most appliances (98%), improves the appliance recognition performance in F-measure by at least 41.1%, and reduces the Mean Absolute Error (MAE) of energy disaggregation by at least 30.4% over other alternative solutions.