Near Real-Time Low Frequency Load Disaggregation
Keywords:NILM, Load Disaggregation, Multi-label Classification, Multi-output Regression, Energy Estimation, Smart Meters
Device-level power consumption information can lead to considerable energy savings. Smart meters are being adopted in several countries, but they are only capable of measuring the total power consumption. NonIntrusive Load Monitoring (NILM) aims to infer the power consumption of individual electrical loads by analyzing the aggregate power signal taken from a single-point measurement. Most existing NILM solutions are offline methods that do not allow the end-user to get real-time feedback on his energy consumption. In this paper, we present a near real-time NILM solution based on multi-label classification and multi-output regression. We use the multi-label classifier to predict the state of each load and use the multi-output regressor to estimate the disaggregated active power consumptions. We test our method using a publically available dataset of real power measurements. Performance results show that the proposed near real-time method can accurately estimate the energy consumption of the targeted loads with an average relative energy error of 1.55 %.
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