Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances

Non-intrusive load monitoring (NILM) provides a cost-effective solution for smart services across numerous appliances by inferring appliance-level information from mains electrical measurements. With the rapid growth in appliance diversity, continual learning that adapts to new appliances while retaining knowledge of previously learned appliances is of great interest. However, existing methods can handle only a few new appliance types and suffer from high computational complexity and data leakage risks. Therefore, an analytic continual learning-based (ACL) NILM method is proposed. The method employs a lightweight model that is constructed with dual output branches using depthwise separable convolution for load identification and novelty detection. Meanwhile, a supervised contrastive learning strategy is applied to enhance the distinctiveness among appliance types in the feature extraction module. When the novelty detection branch determines that new data need to be learned, the parameters of the dual branches are updated by recursively calculating the analytical solution using only the current data. Experiments on four public datasets demonstrate superior performance on pre-collected appliances with lower computational effort. It also significantly outperforms existing methods during the continual learning process, as the number of appliance types increases to 56. The practicality of the proposed method is validated through a real-world application on an STM32F407-based smart socket.