Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion
This work addresses real-world NILM deployment issues like overfitting and low generalization for household energy monitoring, but it is incremental as it builds on existing methods.
The paper tackles challenges in non-intrusive load monitoring (NILM) by proposing a lightweight model with a novel PCA-ICA feature fusion method, achieving higher F1 scores and robustness with up to 15 concurrently active appliances.
Non-intrusive load monitoring (NILM) is an advanced load monitoring technique that uses data-driven algorithms to disaggregate the total power consumption of a household into the consumption of individual appliances. However, real-world NILM deployment still faces major challenges, including overfitting, low model generalization, and disaggregating a large number of appliances operating at the same time. To address these challenges, this work proposes an end-to-end framework for the NILM classification task, which consists of high-frequency labeled data, a feature extraction method, and a lightweight neural network. Within this framework, we introduce a novel feature extraction method that fuses Independent Component Analysis (ICA) and Principal Component Analysis (PCA) features. Moreover, we propose a lightweight architecture for multi-label NILM classification (Fusion-ResNet). The proposed feature-based model achieves a higher $F1$ score on average and across different appliances compared to state-of-the-art NILM classifiers while minimizing the training and inference time. Finally, we assessed the performance of our model against baselines with a varying number of simultaneously active devices. Results demonstrate that Fusion-ResNet is relatively robust to stress conditions with up to 15 concurrently active appliances.