  Non-intrusive load monitoring (NILM) is a modern and still expanding
technique, helping to understand fundamental energy consumption patterns and
appliance characteristics. Appliance event detection is an elementary step in
the NILM pipeline. Unfortunately, several types of appliances (e.g., switching
mode power supply (SMPS) or multi-state) are known to challenge
state-of-the-art event detection systems due to their noisy consumption
profiles. Classical rule-based event detection system become infeasible and
complex for these appliances. By stepping away from distinct event definitions,
we can learn from a consumer-configured event model to differentiate between
relevant and irrelevant event transients.
  We introduce a boosting oriented adaptive training, that uses false positives
from the initial training area to reduce the number of false positives on the
test area substantially. The results show a false positive decrease by more
than a factor of eight on a dataset that has a strong focus on SMPS-driven
appliances. To obtain a stable event detection system, we applied several
experiments on different parameters to measure its performance. These
experiments include the evaluation of six event features from the spectral and
time domain, different types of feature space normalization to eliminate
undesired feature weighting, the conventional and adaptive training, and two
common classifiers with its optimal parameter settings. The evaluations are
performed on two publicly available energy datasets with high sampling rates:
BLUED and BLOND-50.
