Load curve data is a type of time series data which records the electric energy consumptions at time points and plays an important role in operation and planning of power systems. Unfortunately, load curves always contain abnormal, noisy, unrepresentative and missing data due to various random factors. It is crucial to power systems to identify and repair corrupted and unrepresentative data before load curve data can be used for planning and modeling. In this thesis we present a new class of X-outliers that have abnormal power consumption levels related to periodicity (X-axis) and propose a novel solution to detect these outliers. The underlying assumption is that the data set follows a periodicity and the length (not the pattern) of the periodicity is known. This is the case for most real load curve data collected at BC Hydro. In the above the periodicity is assumed to be known for X-outlier detection. In some other applications, however, the periodicity needs to be discovered. The latter is the case when the periodicity evolves, when a new time series is collected, or when conditions that affect time series have changed. Periodicity detection for time series has important applications in forecasting, planning, trend detection, and outlier detection. For time series with unknown periodicity, X-outlier detection could still be performed after the periodicity is detected. Thus X-outlier detection and periodicity detection are highly related and periodicity detection could be considered as a pre-processing step of X-outlier detection for time series with unknown periodicity. Therefore, in this thesis, we also propose a trend based periodicity detection algorithm for time series data with unknown periodicity. This approach is trend preserving and noise resilient. Real load curve data in the BC Hydro system is used to demonstrate the eectiveness and accuracy of the proposed methods.
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Thesis advisor: Wang, Ke
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