# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 1 Introduction
To avoid storing original appliance data, Ref. [13] leverages a generative model to recreate waveforms for joint fine-tuning with new data. But, the generative model may provide a biased representation of the original data and is difficult to update continually in streaming data. Moreover, ref. [14] applies a knowledge distillation loss on the original data during fine-tuning and scales the model's output layer according to the parameter ratios of new and original appliance types to mitigate forgetting. Ref. [15] further selects model layers for dynamically updating based on the loss, but this significantly increases the complexity of continual learning. However, all existing continual learning-based NILM methods rely on storing substantial original data or generators, which not only burdens computational resources but also raises the risk of sensitive appliance data leakage. Furthermore, despite storing original data, the monitoring performance of existing methods still degrades rapidly as more new appliances are added. Before the continual learning of load identification models, it is also necessary to detect whether the incoming data contain new appliances, which is known as novelty detection. In real-world scenarios, streaming data often include both new and previously seen appliances. Novelty detection helps prevent unnecessary model updates. It also prevents new appliance types from being identified as existing types in event-based NILM. Existing novelty detection methods focus on similarity matching using either raw waveforms or extracted features. Ref. [16] designs complex matching rules based on dynamic time warping, but they are limited to low-frequency power data and are sensitive to noise. Ref. [17,18] transform appliance waveforms into images, which are then matched in feature space using Euclidean distance. In contrast, ref. [19] uses DBSCAN to adaptively detect new data based on density in the feature space, avoiding manual tuning of distance thresholds. To enhance the flexibility of similarity computation, Ref. [20] uses a neural network in the feature space of the Siamese network to compute pairwise similarity. However, these methods are not only inefficient in similarity matching but also rely on storing large amounts of original data, facing the same critical challenges as continual learning-based load identification methods in streaming data. Moreover, these novelty detection methods are not yet capable of continual learning. As the NILM model learns new appliances, the novelty detection method must also be updated to prevent misclassifying previously learned appliances as new in subsequent streaming data. To handle new appliances in streaming data while avoiding the complexity and data leakage risks of data storage, an analytic continual learning-based NILM method is proposed. To the best of our knowledge, this is the first NILM method that eliminates dependency on original data during continual learning while providing theoretical robustness against catastrophic forgetting. The contributions of this paper are summarized as follows:  
1. An analytic, continual learning-based framework adaptive to diverse new appliances is proposed. This framework establishes a closed-loop iteration between novelty detection and continual learning for streaming appliance data. It eliminates original data storage requirements and learns new appliances through a single forward propagation. 2. A unified model is designed featuring a depthwise separable convolutional feature extractor and dual output branches for load identification and novelty detection. Crucially, the novelty detection branch represents the original data via a hypersphere center and radius in feature space, avoiding the need for data storage. 3. A supervised contrastive learning-based pretraining strategy is proposed to enhance intra-type clustering and inter-type separation in feature space.