Appliance-Incremental Learning for Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) is an algorithmic-based approach for monitoring appliances inside a building using a single metering point, enabling better traceability of energy use within the grid. Most state-of-the-art approaches, that are based on Deep Neural Networks (DNN), are “static” and present serious limitations in handling changes in appliance usage patterns or the addition of new appliances. This limits the adaptability of NILM in practical scenarios. To address this, we propose an Appliance-Incremental Learning (AIL) method that can continuously adapt a DNN-based NILM algorithm to monitor new appliances without introducing new networks. AIL utilizes a multi-label classification framework, sharing the same network structure for all appliances. We mitigate catastrophic forgetting by distilling knowledge of previous tasks and dynamically selecting network parameters better related to new tasks. This allows us to efficiently train the network on new tasks while maintaining stable performance on previous appliances. We compare our method with the static NILM approach and the Learning without Forgetting (LwF) method in a real-world scenario, where a pre-trained model is adapted to new environments post-deployment. The results demonstrate the effectiveness of our method in handling new appliances, achieving improved adaptability and performance with respect to comparative methods. The proposed AIL method offers a reliable solution for NILM, enabling the monitoring of new appliances and accommodating variations in usage patterns.