ContiNILM: A Continual Learning Scheme for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is considered an efficient approach to infer the consumption pattern of household appliances from the aggregate consumption signal. Continual adaptability is an important aspect of practical NILM applications, as they usually require frequent post-deployment maintenance to deal with non-stationary appliances’ data distributions. However, in most approaches, the trained deep learning model weights remain static, potentially neglecting valuable information that can be used for further model training. This work alleviates the aforementioned limitation by introducing ContiNILM, a continual learning scheme for NILM to build robust models that track environmental/seasonal alterations with direct impact in several appliances’ operation. In our approach, model weights do not remain static, but utilize additional training data to further improve the disaggregation performance. A novel mechanism is proposed that determines whether new incoming samples would be beneficial for model training and alleviates the risk of "forgetting" previously learned knowledge. Experimental results demonstrate the efficiency of the proposed approach.