Background: The traditional state-based non-intrusive load monitoring method mainly
deploys the aggregate load as the characteristic to identify the states of every electrical appliance. Each
identification is relatively independent, and there is no correlation between the identification results.
Objective: This paper combines the event detection results with the state-based non-intrusive load
identification algorithm to improve accuracy.
Methods: Firstly, the load recognition model based on an artificial neural network is constructed, and
the state-based recognition results are obtained. An event recognition and detection model is then
built to identify electrical state transitions, that is, the current moment based on the event recognition
results obtained from the previous moment. Finally, a reasonable decision method is constructed to
determine the identification result of the electrical states.
Results: Experimental results on the public data set REDD show that in the Long Short-Term
Memory (LSTM) fusion model, the macro-F1 is increased by an average of 6%, and the macro-F1 of
the Artificial Neural Network (ANN) fusion model is increased by an average of 5.3% compared with
LSTM and ANN.
Conclusion: The proposed model can effectively improve the accuracy of identification compared
with the state-based load identification method.