  Non-Intrusive Load Monitoring (NILM) is a practical method to provide
appliance-level electricity consumption information. Event detection, as an
important part of event-based NILM methods, has a direct impact on the accuracy
of the ultimate load disaggregation results in the entire NILM framework. This
paper presents a hybrid event detection approach for relatively complex
household load datasets that include appliances with long transients, high
fluctuations, and/or near-simultaneous actions. The proposed approach includes
a base algorithm based on moving average change with time limit, and two
auxiliary algorithms based on derivative analysis and filtering analysis. The
structure, steps, and working principle of this approach are described in
detail. The proposed approach does not require additional information about
household appliances, nor does it require any training sets. Case studies on
different datasets are conducted to evaluate the performance of the proposed
approach in comparison with several existing approaches including log
likelihood ratio detector with maxima (LLD-Max) approach, active window-based
(AWB) approach, and generalized likelihood ratio (GLR) approach. Results show
that the proposed approach works well in detecting events in complex household
load datasets and performs better than the existing approaches.
