# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 4 Experiments and Analysis
### Validation of Hardware Deployment in Real-World Settings  
To validate the proposed method in real-world environments, hardware deployment was implemented through pretraining, C code conversion, and chip programming, with the designed hardware being a smart socket comprising a high-power circuit board and a low-power circuit board interconnected via stacked pin headers to conserve space, as illustrated in Figure 6a,b. The core components of the high-power board include a plugin, power supply, relay, and sensor, while the low-power board handles data measurement, computation, and storage, utilizing an STM32F407 microcontroller (1 MB flash, 192 KB RAM) (STMicroelectronics, Geneva, Switzerland) as the edge computing core, a BL0956 chip (Belling, Shanghai, China) for high-frequency voltage/current sensing, an ESP32 Wi-Fi chip (Espressif Systems, Shanghai, China) for remote communication, and a W9825G6K chip (Winbond Electronics, Hsinchu, China) for expanded storage to enable NILM (non-intrusive load monitoring) feedback and manual appliance-type labeling. More than 21 types and a total of 151 appliances were prepared to mimic continual learning. For pretraining, device types with larger sample volumes were prioritized, with the streaming data partitioning detailed in Table 3. The overall partitioning principle is as follows: categories containing over 100 samples were allocated for pretraining, while the remaining classes, sorted in descending order by sample count, were grouped into triplets to form continual learning datasets. This approach stems from the observation that devices with higher sample volumes typically represent common real-world appliances, thereby facilitating easier access to trainable data. Conversely, devices with sparse samples are less likely to be captured during initial sampling; they are gradually integrated and discovered through prolonged monitoring, making them more suitable as incremental targets for continual learning. To ensure the presence of new appliances in the novelty detection experiment, the \(D_{5}\) dataset was retained. As in previous experiments, about 20% of the appliances in each type are reserved for testing. For new appliances, the waveforms to be learned are saved locally, and the type labels are sent manually to the hardware via the host computer. After the hardware completes continual learning, the appliances are manually plugged in or unplugged for testing. The experimental environment is shown in Figure 6c. Figure 5: The average performance with and without SCL-based pretraining strategy: (**a**) load identification (ACC); (**b**) load identification (F1-macro); (**c**) novelty detection (ACC); (**d**) novelty detection (F1-macro). \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Appliance Types in the Dataset**} & **Role** \\ \hline D0: Hair dryer, induction cooker, fan, rice cooker, kettle, charger & Pretraining \\ D1: Disinfection cabinet, fridge, LED lamp & Continual learning \\ D2: Humidifier, iron, network switch & Continual learning \\ D3: Stove, TV, dehumidifier & Continual learning \\ D4: Electric bicycle, microwave, juice maker & Continual learning \\ D5: Electric blanket, incandescent light bulb, heater & Continual learning \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The division of appliance types in streaming data. The results are shown in Figure 7. In load identification, the similarity in operating patterns between power electronic appliances (e.g., chargers, computers, TV) and resistive appliances (e.g., disinfection cabinet, rice cooker, heater) increases identification difficulty, reducing performance by about 10% compared to the public datasets. Despite this, load identification performance fluctuates by less than 5% during continual learning as the number of appliance types expands to 21, making it feasible for practical application.