Abstract
Information about power consumption patterns of devices, helps the residential consumers manage their energy usage. Nonintrusive load monitoring is an effective tool to extract the consumption patterns from the measured aggregated data at the meter. In this paper, an optimization-based method is proposed to disaggregate the total load, using low frequency data. The proposed algorithm is enhanced by enforcing the power profiles of appliances to be piecewise constant over specific time durations. Moreover, the state transitions of the appliances are determined and then employed as the optimization constraints to improve the estimation results. The proposed method is evaluated using almanac of minutely power data set (AMPds) and reference energy disaggregation data set (REDD) datasets by several performance metrics. Results indicate that the designed algorithm is able to recognize the frequently varying appliances in spite of piecewise constancy presumption. Furthermore, breaking down the optimization problem to the smaller parts enhances the ability of the algorithm to be operated in real time.
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