Automated load curve data cleansing in power systems

Load curve data refers to power 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. It is unavoidable that load curves contain corrupted data and missing data due to various random failure factors in meters and transfer processes. In this thesis, nonparametric smoothing techniques are proposed to model the load curve data and detect corrupted data. An adapted multiplicative model is built to correct corrupted data and fill in missing data. In implementation, an incremental training procedure is proposed to enhance the performance. The experiment results on the real BCTC (British Columbia Transmission Corporation) load curve data demonstrated the effectiveness of the presented solution.