  High-resolution data are desired in many data-driven applications; however,
in many cases only data whose resolution is lower than expected are available
due to various reasons. It is then a challenge how to obtain as much useful
information as possible from the low-resolution data. In this paper, we target
interval energy data collected by Advanced Metering Infrastructure (AMI), and
propose a Super-Resolution Reconstruction (SRR) approach to upsample
low-resolution (hourly) interval data into higher-resolution (15-minute) data
using deep learning. Our preliminary results show that the proposed SRR
approaches can achieve much improved performance compared to the baseline
model.
