A Distributed and Scalable Approach to Semi-Intrusive Load Monitoring

Abstract

Non-intrusive appliance load monitoring (NIALM) helps identify major energy guzzlers in a building without introducing extra metering cost. It motivates users to take proper actions for energy saving and greatly facilitates demand response (DR) programs. Nevertheless, NIALM of large-scale appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary large-scale appliance groups, we propose a distributed metering platform and use parallel optimization for semi-intrusive appliance load monitoring (SIALM). Based on a simple power model, a sparse switching event recovering (SSER) model is established to recover appliance states from their aggregated load data. Furthermore, the sufficient conditions for unambiguous state recovery of multiple appliances are presented. By considering these conditions as well as the electrical network topology constraint, a minimum number of meters are obtained to correctly recover the energy consumption of individual appliances. We evaluate the performance of both SIALM and NIALM with real-world trace data and synthetic data. The results demonstrate that with the help of a small number of meters, the SIALM approach significantly improves the accuracy of energy disaggregation for large-scale appliances.