Non-intrusive load monitoring system performance over a range of low frequency sampling rates

Abstract

Non-intrusive load monitoring (NILM) systems estimate the amount of energy each appliance consumes using as input the aggregate building energy consumption. Typically, NILM results are presented for a single sampling rate. To evaluate tradeoffs between end-uses and sensor costs, it is important to study the performance of NILM systems across sampling rates. In this work, we examine the performance of two NILM systems over a range of low frequency sampling rates on two datasets. Our results empirically demonstrate how NILM classification performance degrades nonlinearly as the sampling rate decreases and how varied this degradation is across appliance types. The results also suggest that reporting algorithm accuracy for a single sampling rate may not be sufficient for thorough algorithm performance evaluation. These findings can assist policy and decision makers in identifying the right smart meter hardware to meet appliance-level energy efficiency and building automation goals.