Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance
Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance
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DOI：
10.1016/j.epsr.2016.05.023
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发表时间：
2016-11
影响因子：
3.9
通讯作者：
Ignacio Benítez;J. Díez;A. Quijano;Ignacio Delgado
中科院分区：
文献类型：
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作者：
Ignacio Benítez;J. Díez;A. Quijano;Ignacio Delgado
随着电力负荷分析达到从低压配电网测量的数据，参与电网运营和管理的主要代理需要根据最终用户的日常能源消耗形状或负荷分布对最终用户进行细分，并获得允许根据用户消耗能源的方式对用户进行分组的模式。然而，这种分析通常限于对单个日的分析。由于智能计量数据是通过一天中的每个小时或一刻钟以及每天的能量的顺序测量形成的时间序列，因此由于高级计量基础设施（AMI）和智能电网技术的实施，很明显，数据的分析需要扩展以考虑消费模式在几天、几周、几个月、几个季节、几个月这是本工作的目标。提出了一个新的框架，解决动态聚类，可视化和识别的时间模式的负载配置文件的时间序列，履行检测到的差距在这方面。目前的发展是一个通用的框架，允许聚类和可视化的负载配置文件的时间序列应用不同的经典聚类算法。一种新的动态聚类算法也提出了，基于初始分割的能源消耗的时间序列数据在较小的表面，和它们之间的相似性度量的计算应用Hausdorff距离。以下，这些发展提出和测试的两个数据集的能源消耗负荷配置文件从一个样本的住宅用户在西班牙和伦敦。
As the analysis of electrical loads is reaching data measured from low voltage power distribution networks, there is a need for the main agents involved in the operation and management of the power grids to segment the end users as a function of their shapes of daily energy consumption or load profiles, and to obtain patterns that allow to classify the users in groups based on how they consume the energy.However, this analysis is usually limited to the analysis of single days. Since the smart metering data are time series formed by sequential measurements of energy through each hour or quarter of hour of the day, and also through each day, thanks to the implementation of Advanced Metering Infrastructure (AMI) and the Smart Grid technologies, it becomes clear that the analysis of the data needs to be extended to consider the dynamic evolution of the consumption patterns through days, weeks, months, seasons, and even years.This is the objective of the present work. A new framework is presented that addresses the dynamic clustering, visualization and identification of temporal patterns in load profiles time series, fulfilling the detected gap in this area. The present development is a generic framework that allows the clustering and visualization of load profiles time series applying different classical clustering algorithms. A novel dynamic clustering algorithm is also presented, based on an initial segmentation of the energy consumption time series data in smaller surfaces, and the computation of a similarity measure among them applying the Hausdorff distance. Following, these developments are presented and tested on two dataset of energy consumption load profiles from a sample of residential users in Spain and London.