Energy Disaggregation with Semi-supervised Sparse Coding
This addresses the need for device-level energy monitoring to help consumers conserve energy, representing an incremental improvement in a domain-specific application.
The paper tackles the problem of decomposing aggregated household energy consumption data into individual appliance usage, evaluating a discriminative sparse coding model on a large-scale dataset. The model achieved improved energy disaggregation performance compared to benchmark models, though no specific numerical results are provided in the abstract.
Residential smart meters have been widely installed in urban houses nationwide to provide efficient and responsive monitoring and billing for consumers. Studies have shown that providing customers with device-level usage information can lead consumers to economize significant amounts of energy, while modern smart meters can only provide informative whole-home data with low resolution. Thus, energy disaggregation research which aims to decompose the aggregated energy consumption data into its component appliances has attracted broad attention. In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation. We utilize a structured prediction model for providing discriminative sparse coding training, accordingly, maximizing the energy disaggregation performance. Designing such large scale disaggregation task is investigated analytically, and examined in the real-world smart meter dataset compared with benchmark models.