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A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households

机译：家庭用电模式分析的多功能聚类方法

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摘要

Analysis and modeling of electric energy demand is indispensable for power planning, operation, facility investment, and urban planning. Because of recent development of renewable energy generation systems oriented for households, there is also a great demand for analysing the electricity usage and optimizing the way to install electricity generation systems for each household. In this study, employing statistical techniques, a method to model daily consumption patterns in households and a method to extract a small number of their typical patterns are presented. The electricity consumption patterns in a household is modeled by a mixture of Gaussian distributions. Then, using the symmetrized generalized Kullback-Leibler divergence as a distance measure of the distributions, typical patterns of the consumption are extracted by means of hierarchical clustering. The statistical modeling of daily consumption patterns allows us to capture essential similarities of the patterns. By experiments using a large-scale dataset including about 500 houses' consumption records in a suburban area in Japan, it is shown that the proposed method is able to extract typical consumption patterns.
机译：电力需求分析和建模对于电力规划，运营，设施投资和城市规划必不可少。由于最近开发了面向家庭的可再生能源发电系统，因此也存在着对用电量进行分析并优化每个家庭的发电系统安装方式的需求。在这项研究中，采用统计技术，提出了一种模拟家庭日常消费模式的方法以及一种提取少量典型模式的方法。一个家庭的用电量模式是由高斯分布的混合模型建立的。然后，使用对称的广义Kullback-Leibler发散作为分布的距离度量，通过层次聚类提取典型的消费模式。日常消费模式的统计模型使我们能够捕获模式的本质相似性。通过使用包含日本郊区约500栋房屋消费记录的大规模数据集进行的实验，表明该方法能够提取典型的消费模式。

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