From Landscape to Portrait: A New Approach for Outlier Detection in Load Curve Data

TL;DR

This paper introduces a novel 'portrait' approach for outlier detection in power load curve data, improving speed and accuracy over traditional methods by reorganizing data based on periodic patterns.

Contribution

The paper presents a new 'portrait' view and algorithms for load curve data analysis, enhancing outlier detection efficiency and accuracy.

Findings

Faster outlier detection compared to regression-based methods

More accurate analysis for small and large datasets

Effective data cleansing in load curve data

Abstract

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.