# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 1 Introduction  
The smart grid integrates power transmission with information flows, offering the possibility of better electricity usage. However, limited access to appliance-level data renders behind-the-meter electricity usage at the grid's edges a \"black box\". Therefore, non-intrusive load monitoring (NILM), which estimates the operation of appliances only by analyzing mains electrical measurements, has emerged as a major research focus. It eliminates the need for installing separate sensors for each appliance, offering a cost-effective way to obtain fine-grained data [1]. Appliance-level data enable grid operators to design personalized demand-response programs tailored to users' electricity usage patterns. At the same time, it brings direct benefits to users through services such as appliance maintenance, smart homes, and energy management [2]. NILM is categorized into two pathways: event-less and event-based. The former directly decomposes long-term aggregated power consumption into target appliance usage [3]. The latter first detects the leap of mains' measurements in real time, extractsthe corresponding event waveforms, and then estimates the appliance types using a load identification model [4]. The event-based pathway is prioritized due to the following three inherent advantages: (1) It is more suitable for monitoring multiple appliances in a single-load identification model, aligning with the growing diversity of appliances. (2) By focusing on appliance-specific characteristics, identification models designed for single-event waveforms are more robust to external factors such as user behavior. (3) Benefiting from high-frequency sampling, it has a wider range of application scenarios due to its faster response. Although there are many event-based NILM studies, they focus on improving the learning of appliances in pre-collected datasets. However, rapid economic growth and technological advances have led to rich appliance diversity, making it impractical to collect data on all appliances in advance [5]. Therefore, in real-world applications where new users join or when monitoring requirements evolve, NILM models must continually update their parameters to adapt to emerging appliances in streaming data. Crucially, the integration of new appliance types does not negate the need to maintain the identification abilities for existing appliances. This necessitates preserving learned waveform-to-type mapping relationships during streaming data adaptation to mitigate catastrophic forgetting, a phenomenon where acquiring new knowledge degrades performance on original appliances [6]. While existing NILM methods based on transfer learning or meta-learning leverage accumulated knowledge to accelerate adaptation to new appliances [4; 7; 8], these methods exhibit critical limitations in streaming data contexts. Specifically, they are designed for single-round parameter updates using pre-collected target appliance datasets, inherently disregarding the retention of historical knowledge during learning. As a result, the continual learning paradigm for streaming data is attracting growing attention [9]. This paradigm aims to continually improve the identification of new appliances while preserving knowledge of previously learned ones. Current continual learning methods in NILM aim to mitigate catastrophic forgetting by jointly training new data and original data. Pioneering work [10] introduced continual learning into NILM. When new data arrive, this method first evaluates performance to determine whether to randomly select stored data for joint fine-tuning. It then updates the appliance library based on waveform similarity. Ref. [11] employs density-based spatial clustering of applications with noise (DBSCAN) to extract representative waveforms when updating the appliance library, thereby reducing the forgetting of important knowledge more effectively. Ref. [12] selects important waveforms for storage using attention weights from the NILM model, but it is limited to specific model architectures.