  In power systems, load curve data is one of the most important datasets that
are collected and retained by utilities. The quality of load curve data,
however, is hard to guarantee since the data is subject to communication
losses, meter malfunctions, and many other impacts. In this paper, a new
approach to analyzing load curve data is presented. The method adopts a new
view, termed \textit{portrait}, on the load curve data by analyzing the
periodic patterns in the data and re-organizing the data for ease of analysis.
Furthermore, we introduce algorithms to build the virtual portrait load curve
data, and demonstrate its application on load curve data cleansing. Compared to
existing regression-based methods, our method is much faster and more accurate
for both small-scale and large-scale real-world datasets.
