  Load curve data in power systems refers to users' electrical energy
consumption data periodically collected with meters. It has become one of the
most important assets for modern power systems. Many operational decisions are
made based on the information discovered in the data. Load curve data, however,
usually suffers from corruptions caused by various factors, such as data
transmission errors or malfunctioning meters. To solve the problem, tremendous
research efforts have been made on load curve data cleansing. Most existing
approaches apply outlier detection methods from the supply side (i.e.,
electricity service providers), which may only have aggregated load data. In
this paper, we propose to seek aid from the demand side (i.e., electricity
service users). With the help of readily available knowledge on consumers'
appliances, we present a new appliance-driven approach to load curve data
cleansing. This approach utilizes data generation rules and a Sequential Local
Optimization Algorithm (SLOA) to solve the Corrupted Data Identification
Problem (CDIP). We evaluate the performance of SLOA with real-world trace data
and synthetic data. The results indicate that, comparing to existing load data
cleansing methods, such as B-spline smoothing, our approach has an overall
better performance and can effectively identify consecutive corrupted data.
Experimental results also demonstrate that our method is robust in various
tests. Our method provides a highly feasible and reliable solution to an
emerging industry application.
