Abstract
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.
Keywords: Decision tree, event identification, fusion model, neural network, non-intrusive load identification, state-based method.
Graphical Abstract
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