Low frequency residential load disaggregation via improved variational auto-encoder and Siamese network

Non-intrusive load monitoring (NILM) can infer load profiles for each individual appliance from aggregated power consumption signals without installing extra sub-meters. However, the performance of traditional energy disaggregation methods deteriorates in complex environments, especially susceptible to the presence of other high power consumption appliances. The practicalities are also limited by the diversity of household load patterns and measurement errors. In order to address these problems, a hybrid deep learning model consisting of two steps is proposed in this paper. First, an improved variational auto-encoder (VAE) structure is introduced for preliminary energy disaggregation, where the encoder and decoder layers are long short-term networks (LSTM) to extract temporal characteristics of active power signals. Afterward, a post-processing method based on Siamese one-dimensional convolutional neural network (S-1D-CNN) is adopted to remove incorrectly predicted activation segments of the target appliances. Experiments are conducted on two public datasets, and results show remarkable improvements on the prediction accuracy over other deep learning methods. Both transferability and stability of the proposed model are also verified under different working conditions.