Load shape dictionary (LSD) is a useful tool for utilizing the enormous amount of smart meter data to understand customers’ electricity consumption behaviors. To tackle the big data challenge as well as to better capture the load shape features, this article develops a bilevel LSD generation framework to cluster and index the residential load profiles into a neat local LSD and a global LSD based on the Derivative Dynamic Time Warping (DDTW) elastic dissimilarity measure. Different from the classic Dynamic Time Warping (DTW), DDTW works on the derivative of the raw data to avoid DTW’s problem of pathological alignments. To reduce the computational cost, a fast DDTW (FDDTW) is proposed to speed up the DDTW calculation. Based on the generated bilevel LSD, analytic approaches are proposed to extract features from the data indexed by the LSD to reveal useful information of customers’ electricity consumption behaviors. Numerical experiments on real premise data verify the effectiveness of the proposed methodology in terms of clustering performance and computational efficiency. Our analysis suggests that the proposed methodology can be applied to improve load forecasting, tariff design and demand response (DR) customer targeting.

中文翻译：

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基于弹性相异测度的有效聚类开发负荷形状字典

负载形状字典（LSD）是有用的工具，可用于利用大量智能电表数据来了解客户的用电行为。为了解决大数据挑战并更好地捕获负载形状特征，本文开发了一种双层LSD生成框架，以基于导数动态时间规整（ DDTW）弹性差异测度。与经典的动态时间规整（DTW）不同，DDTW可以处理原始数据的导数，从而避免DTW的病理排列问题。为了减少计算成本，提出了一种快速DDTW（FDDTW）来加快DDTW计算的速度。根据生成的双层LSD，提出了一种分析方法来从LSD索引的数据中提取特征，以揭示有关客户用电行为的有用信息。在实际前提数​​据上的数值实验证明了该方法在聚类性能和计算效率方面的有效性。我们的分析表明，所提出的方法可用于改善负荷预测，电价设计和需求响应（DR）客户目标。