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Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles

机译:使用所选聚类方法的原始数据时间分辨率对住宅用电负荷曲线的影响

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There is growing interest in discerning behaviors of electricity users in both the residential and commercial sectors. With the advent of high-resolution time-series power demand data through advanced metering, mining this data could be costly from the computational viewpoint. One of the popular techniques is clustering, but depending on the algorithm the resolution of the data can have an important influence on the resulting clusters. This paper shows how temporal resolution of power demand profiles affects the quality of the clustering process, the consistency of cluster membership (profiles exhibiting similar behavior), and the efficiency of the clustering process. This work uses both raw data from household consumption data and synthetic profiles. The motivation for this work is to improve the clustering of electricity load profiles to help distinguish user types for tariff design and switching, fault and fraud detection, demand-side management, and energy efficiency measures. The key criterion for mining very large data sets is how little information needs to be used to get a reliable result, while maintaining privacy and security.
机译:人们越来越意识到在住宅和商业领域中用电者的行为。随着通过高级计量的高分辨率时间序列电力需求数据的出现,从计算的角度来看,挖掘这些数据可能会变得昂贵。一种流行的技术是聚类,但是根据算法,数据的分辨率可能对所得的聚类产生重要影响。本文展示了电力需求曲线的时间分辨率如何影响集群过程的质量,集群成员的一致性(曲线表现出相似的行为)以及集群过程的效率。这项工作使用了来自家庭消费数据的原始数据和综合资料。这项工作的动机是改善电力负荷分布图的聚类,以帮助区分用户类型,以进行电价设计和转换,故障和欺诈检测,需求侧管理以及能效措施。挖掘非常大的数据集的关键标准是在维护隐私和安全性的同时,只需很少的信息即可获得可靠的结果。

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