TimeNILM: Time-Aware Dual-CNN Model for Non-Intrusive Load Monitoring

Thoroughly monitoring the energy consumption of each appliance in a household is essential to assist users in better engaging in power-saving practices. To get per-appliance electrical profile, non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation task, that decomposes the total household electricity consumption into the load profile of each individual appliance. In recent years, deep learning models have demonstrated superior performance in NILM tasks. However, when encountering an unseen household, the model performance degrades seriously. We observe that temporal information is beneficial for the model generalization ability, as the ON/OFF patterns of electrical appliances follow a similar time schedule across different households. Thus, in this paper, we propose a time-aware dual-CNN architecture for NILM (TimeNILM), which fully exploits the temporal information and employs feature fusion based on attention mechanism to improve model performance. Experimental results on two datasets (REDD and UK-DALE) demonstrate that our model outperforms state-of-the-art models, achieving 8%-27% MAE gain and 8%-35% SAE<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</inf> gain for unseen households.