Load event detection is the fundamental step for the event-based
non-intrusive load monitoring (NILM). However, existing event detection methods
with fixed parameters may fail in coping with the inherent multi-timescale
characteristics of events and their event detection accuracy is easily affected
by the load fluctuation. In this regard, this paper extends our previously
designed two-stage event detection framework, and proposes a novel
multi-timescale event detection method based on the principle of minimum
description length (MDL). Following the completion of step-like event detection
in the first stage, a long-transient event detection scheme with
variable-length sliding window is designed for the second stage, which is
intended to provide the observation and characterization of the same event at
different time scales. In that, the context information in the aggregated load
data is mined by motif discovery, and then based on the MDL principle, the
proper observation scales are selected for different events and the
corresponding detection results are determined. In the post-processing step, a
load fluctuation location method based on voice activity detection (VAD) is
proposed to identify and remove the unreasonable events caused by fluctuations.
Based on newly proposed evaluation metrics, the comparison tests on public and
private datasets demonstrate that our method achieves higher detection accuracy
and integrity for events of various appliances across different scenarios.Comment: 11 pages,16 figure