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Electricity Usage Clustering with K-means++ Algorithm

机译:基于K-means++算法的用电量聚类

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摘要

Global electricity consumption is increasing every year. Electricity has become a necessary need for every sector, from a household to government and industry. With today's technology, data also has become more important, including electricity data. The right tools and techniques can extract valuable information from data. Using a K-Means++ algorithm to cluster electricity data can help to determine when the usage is low, moderate, and high. In this study, there three scenarios of clustering; hourly, daily, and monthly. The silhouette score of this experiment ranges from 0.68 to 0.71, and the DB Index ranges from 0.30 to 0.51.
机译:全球用电量每年都在增加。从家庭到政府和工业,电力已成为每个部门的必要需求。随着当今技术的发展,数据也变得越来越重要,包括电力数据。正确的工具和技术可以从数据中提取有价值的信息。使用K-Means++算法对电力数据进行聚类有助于确定使用率是低、中、高。在本研究中,有三种聚类场景;每小时、每天和每月。这个实验的轮廓分数在0.68到0.71之间,DB指数在0.30到0.51之间。

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