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