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Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications

机译：面向大数据应用的用电行为动态聚类

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摘要

In a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities to enhance their knowledge of customers' electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, symbolic aggregate approximation is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique by fast search and find of density peaks (CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured by the Kullback-Liebler distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated into a divide-and-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.
机译：在竞争激烈的零售市场中，大量智能电表数据为负载服务实体提供了机会，可通过负载分析来增强其对客户用电行为的了解。本文不关注负荷曲线的形状，而是提出了一种新颖的电力消费行为动态聚类方法，其中“动态”指的是相邻时期内消费行为之间的转换和关系，更确切地说是消费水平。首先，对于每个单独的客户，执行符号集合近似以减少数据集的规模，然后基于时间的马尔可夫模型被用于模拟用电量的动态变化，将负载曲线的大型数据集转换为多个状态转换矩阵。其次，主要通过快速搜索和发现密度峰值（CFSFDP）进行聚类技术，以获取典型的消费行为动态，并通过Kullback-Liebler距离衡量任意两种消费模式之间的差异，并对客户进行分类分成几个集群。为了应对大数据的挑战，CFSFDP技术已集成到针对大数据应用的分而治之方法中。数值案例验证了所提出模型和方法的有效性。

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