  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.
