Abstract

Non-intrusive load monitoring (NILM) enables appliance-level disaggregation from a single household meter, yet existing Seq2point-based methods are plagued by inadequate noise robustness, unsatisfactory state recognition, and limited cross-dataset generalization. This paper proposes a denoising multi-task convolutional neural network that fundamentally differs from prior approaches by coupling a denoising autoencoder with task learning through a shared reconstruction head—rather than treating denoising as an isolated preprocessor or simply stacking independent loss branches. This design forces the shared feature extractor to preserve fine-grained temporal signal fidelity while jointly optimizing power regression and state classification, thereby imposing an implicit regularization that suppresses noise interference and enhances transferable representation. The model is evaluated on UK-DALE and REDD datasets, achieving MAE/F1 scores of 12.91 W/84.75% and 5.02 W/96.99%, respectively. Ablation studies confirm the synergistic gains from the joint reconstruction–regression–classification paradigm. Furthermore, statistical analysis across five typical appliance types (e.g., kettle, washing machine, and refrigerator) against three state-of-the-art Seq2Point variants reveals that the proposed method yields consistently superior MAE and F1 improvements with statistical significance (paired Wilcoxon test, p < 0.05) and markedly lower performance variance, demonstrating robust efficacy across diverse load profiles. These results substantiate the proposed model as a reliable and statistically validated solution for fine-grained residential energy management in smart grids.

IPC Classification

Keywords