GRU-MF: A Novel Appliance Classification Method for Non-Intrusive Load Monitoring Data

Abstract

Appliance classification using non-intrusive load monitoring (NILM) data is a growing research interest. Various studies in the field have used methods such as long short-term memory (LSTM), recurrent neural network (RNN), convolutional neural network (CNN), and deep neural network (DNN). However, there is a research opportunity to apply a gated recurrent unit (GRU), which is good for low-frequency data, with filtering mode (MF) for smoothing prediction results. This study proposes a novel GRU - MF method for classifying electricity appliances using power data from NILM. The first step in this research is to get NILM data. We use power data from the dishwasher, heater, refrigerator, and lighting. Then the first stage of data pre-processing consists of auto-correlation and time series-data transformation processes. The second stage of pre-processing data consists of normalization, standardization, label encoding, and one hot encoding process. The next stage is GRU training, where we compare the GRU with four benchmark methods: LSTM, CNN, DNN, and RNN. We tested the performance of our proposed model with Accuracy, Precision, and Recall. Finally, we implement MF to improve the performance of our appliance classification model. The test results show that our novel method is better than the LSTM, RNN, CNN, and DNN models. The GRU model itself has an Accuracy equal to 0.96 on test data. Once combined into GRU-MF, we achieve the Accuracy of 0.98 in real data.