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Load curve data records the electric energy consumptions at time intervals 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 identify and repair such data. Previous works focused on detecting Y-outliers that are unusual power consumptions (Y-axis) in a small temporal neighborhood. This paper presents a different class of X-outliers that have abnormal power consumptions according to a known periodicity (X-axis). 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. We discuss possible causes of X-outliers and rationales of identifying and cleaning them. A novel solution to detect and repair X-outliers is proposed. Real load curve data in the BC Hydro system are used to demonstrate the effectiveness and accuracy of the proposed method.