UniversalNILM: A Semi-supervised Energy Disaggregation Framework using General Appliance Models

Non-intrusive load monitoring (NILM) or energy disaggregation aims to estimate appliance-level energy consumption from the aggregate consumption data of households. While there is significant interest from academia and industry, NILM techniques are still not adopted widely across households. This is mainly because the techniques developed for one household cannot be generalized and applied in other households (applicability), and require tremendous hand-tuning to apply across households (scalability). To overcome the above issues, we propose a novel semi-supervised energy disaggregation framework — UniversalNILM. The key idea of UniversalNILM is to model appliances in a few training houses, which has detailed appliance-level data and transfer this learning on to test houses (blind disaggregation), which has only aggregate house consumption to derive fine-grained appliance energy consumption. UniversalNILM was empirically evaluated across datasets and outperforms the reported accuracy from both state-of-the-art supervised and unsupervised NILM techniques.