A Single-to-Multi Network for Latency-Free Non-Intrusive Load Monitoring

As one of the most important smart grid features, non-intrusive load monitoring (NILM) has become a practical technology for identifying the users' energy consumption behavior. The conventional studies are usually based on the assumption that only one appliance is active or the signature database of all appliances is already known. Existing deep learning-based algorithms need to train a model for each target appliance. This paper, however, proposes an energy disaggregation network (EDNet) with deep encoder-decoder architecture to remove the unrealistic assumptions and reduce the size of the network to achieve latency-free NILM with only one model. Firstly, the blind source separation and mask mechanism used for speech recognition are creatively adopted for energy disaggregation. Then, the on/off states of each target appliance is detected based on the results of energy disaggregation. Finally, a personalized signature database with detailed states is constructed based on dynamic time warping (DTW) with energy disaggregation and state detection results to remove the assumption of NILM's dependence on prior information. Full comparison results with the previous work show that our proposed algorithms outperform state-of-the-art methods. It means that the load consumption behavior of residential users can be monitored with high accuracy without sub-metered information and other prior knowledge. Furthermore, the proposed EDNet has significantly smaller parameters, making the NILM toward offline and real-time load monitoring.