A sequence labeling solution for identifying multiple variable-length events in non-intrusive load monitoring

With the sharp change of global climate, NILM (non-intrusive load monitoring) has been a promising approach to reduce energy consumption and carbon emissions. This paper presents a new load identification method using sequence labeling to identify multiple variable-length events within the sliding window of aggregate power signal simultaneously. Furthermore, the proposed method integrates local features of events themselves with temporal features of the multiple events through CNN (convolutional neural network) and attention network. The experimental results on two public datasets REDD and UKDALE indicate that the proposed method achieves better performance than the existing methods.