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 , 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.
Some of the above methods, especially the regression-based methods, have also been used for load forecasting. Nevertheless, load forecasting and load data cleansing are different applications, and their purposes are different. For load forecasting, as mentioned in @cite_14 , all historical data are trusted and used to forecast the load at a future point in time. For load cleansing, historical records are used to detect corrupted data at a historical point in time, and appropriate values may be needed to replace the corrupted data. Therefore, load cleansing and load forecasting belong to different phases of load analysis: load cleansing comes before load forecasting and provides accurate load information for the latter.
@cite_14
1993863450
Load curve data refers to the electric energy consumption recorded by meters at certain time intervals at delivery points or end user points, and contains vital information for day-to-day operations, system analysis, system visualization, system reliability performance, energy saving and adequacy in system planning. Unfortunately, it is unavoidable that load curves contain corrupted data and missing data due to various random failure factors in meters and transfer processes. This paper presents the B-Spline smoothing and Kernel smoothing based techniques to automatically cleanse corrupted and missing data. In implementation, a man-machine dialogue procedure is proposed to enhance the performance. The experiment results on the real British Columbia Transmission Corporation (BCTC) load curve data demonstrated the effectiveness of the presented solution.