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 effectiveness and accuracy of the proposed methods.;Keywords: Time Series, Load Management, Power Systems, Power Quality, Smoothing Methods, Periodicity Detection.
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机译：负荷曲线数据是一种时间序列数据，可以记录各个时间点的电能消耗，并且在电力系统的运行和规划中起着重要的作用。不幸的是，由于各种随机因素，载荷曲线始终包含异常，嘈杂，无代表性和缺失的数据。对于电力系统而言，在将负载曲线数据用于计划和建模之前，识别并修复损坏的和不具代表性的数据至关重要。在本文中，我们提出了一类新的X异常值，它们具有与周期性（X轴）相关的异常功耗水平，并提出了一种检测这些异常值的新颖方法。基本假设是数据集遵循周期性，并且周期性的长度（不是模式）是已知的。对于在BC Hydro处收集的大多数实际负载曲线数据，情况就是如此；在上面，假定对于X异常检测已知周期。但是，在其他一些应用程序中，需要发现周期性。当周期变化，收集新的时间序列或影响时间序列的条件发生变化时，就是后者。时间序列的周期性检测在预测，计划，趋势检测和离群值检测中具有重要的应用。对于具有未知周期性的时间序列，在检测到周期性之后仍可以执行X异常检测。因此，X异常检测和周期性检测高度相关，并且周期性检测可以被视为针对未知周期性的时间序列的X异常检测的预处理步骤。因此，在本文中，我们还针对具有未知周期性的时间序列数据提出了一种基于趋势的周期性检测算法。这种方法可以保持趋势并具有抗噪能力。 BC Hydro系统中的实际负荷曲线数据用于证明所提出方法的有效性和准确性。关键词：时间序列，负荷管理，电力系统，电能质量，平滑方法，周期性检测。
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