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A Novel NILM Event Detection Algorithm Based on Different Frequency Scales
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AI
DOI：10.1109/TIM.2022.3181897.png)
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Abstract
En 中文
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.
Keywords:
Event detection
Data models
Feature extraction
Forestry
Probabilistic logic
Standards
Sensitivity
Energy consumption
event detection
nonintrusive load monitoring (NILM)
time shift downsampling matching (TSDM)
voting improved isolated forest (VIIF)
Journal
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5.9
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1.9W
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