  Event detection is the first step in event-based non-intrusive load
monitoring (NILM) and it can provide useful transient information to identify
appliances. However, existing event detection methods with fixed parameters may
fail in case of unpredictable and complicated residential load changes such as
high fluctuation, long transition, and near simultaneity. This paper proposes a
dynamic time-window approach to deal with these highly complex load variations.
Specifically, a window with adaptive margins, multi-timescale window screening,
and adaptive threshold (WAMMA) method is proposed to detect events in
aggregated home appliance load data with high sampling rate (>1Hz). The
proposed method accurately captures the transient process by adaptively tuning
parameters including window width, margin width, and change threshold.
Furthermore, representative transient and steady-state load signatures are
extracted and, for the first time, quantified from transient and steady periods
segmented by detected events. Case studies on a 20Hz dataset, the 50Hz LIFTED
dataset, and the 60Hz BLUED dataset show that the proposed method can robustly
outperform other state-of-art event detection methods. This paper also shows
that the extracted load signatures can improve NILM accuracy and help develop
other applications such as load reconstruction to generate realistic load data
for NILM research.
