A Novel NILM Event Detection Algorithm Based on Different Frequency Scales

Nonintrusive load monitoring is a technology that can identify the users' internal energy consumption by using the data measured at a single point on the bus and event detection is a key technical problem that needs to be solved. An algorithm combining probability and expert heuristic models is proposed for event detection in this study, including an event predetection subalgorithm called voting improved isolated forest (VIIF) for high-sensitivity event predetection and an event verification subalgorithm called time shift downsampling matching (TSDM) for high-accuracy event verification. VIIF is used to detect suspicious events quickly from the low-frequency characteristics of the signal; TSDM identifies real events from suspicious events by analyzing high-frequency characteristics of the signal. To evaluate the proposed algorithm, three datasets are used. Compared with the state-of-the-art algorithms, the proposed algorithm has great adaptability and accuracy to long-transient events and small-signal events.

Automated summary

Nonintrusive load monitoring is a technology that identifies users' energy consumption using data measured at a single point. This study proposes an event detection algorithm combining probability and expert heuristic models, which achieves high sensitivity and accuracy.