Efficient Energy Disaggregation via Residual Learning-Based Depthwise Separable Convolutions and Segmented Inference

Energy disaggregation is a pivotal task in non-intrusive load monitoring, involving the separation of individual appliance contributions from aggregated energy consumption. At present, deep neural networks are extensively employed for the resolution of this problem, eliciting salutary effects. Unfortunately, this resolution demands a wealth of computational and storage resources. Therefore, energy disaggregation models and schemes characterized by low demands and high performance are anticipated. In this article, we propose a novel lightweight energy disaggregation model by incorporating residual learning and deep separable convolutions while achieving comparable performance to state-of-the-art models. Furthermore, an efficient segmented prediction scheme is proposed to reduce the execution frequency in model applications and meet the real-time requirements of nonintrusive load monitoring. The experimental results on publicly available datasets demonstrate that the proposed method reduces computation by 99.17% compared to state-of-the-art models, while the average of mean absolute error for all appliances increases by only 0.727 W.