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电力大数据:2019,22(03):-
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智能用电数据的采集与预处理
(1.贵州电网有限责任公司遵义供电局;2.贵州理工学院)
Power data acquisition and pre-processing of smart electric appliance network
(1.Zunyi Power Supply Bureau of Guizhou Electric Power Corporation;2.Guizhou Institute of Technology)
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投稿时间:2018-11-01    修订日期:2018-11-25
中文摘要: 解决好智能用电网络数据采集和传输过程中的数据缺失和噪声问题,提高其用电数据的数据质量,才能在智能用电云平台中有效的运用各种用电大数据分析与预测算法。本文在总结智能用电网络的数据采集与数据传输特点,及分析智能用电云平台对用电数据的数据质量要求的基础上,提出了智能用电网络的用电数据预处理方法。对智能用电终端采集的用电数据归一化处理后,利用聚类算法从噪声、模糊、随机数据中提取出正常数据,本文对比验证了K-均值聚类和基于密度的空间聚类两种算法的聚类效果。相比K-均值聚类算法,密度的空间聚类两种算法在检测数据噪声点的同时,可自动获取复杂形状数据集的聚类数量,更适合智能用电网络的用电数据预处理。
Abstract:Solved the problem of data loss and noise in the process of data collection and transmission of smart electric appliance network, and improved the power data quality,which can be effectively use all kinds of big data analysis and prediction algorithms for the electric appliance cloud platform. Based on the summary of the characteristics of data collection and data transmission of the smart electric appliance network, and the analysis of the data quality requirements of the cloud platform, this paper proposes a method of power data preprocessing of the smart electric appliance network.After normalizing the electric appliance data collected by smart electric appliance terminals, normalization data are extracted from noise, fuzzy and random data by means of clustering algorithm, and the clustering effect of k-means clustering and density based spatial clustering of applications with noise (DBSCAN) algorithms is compared and verified. Compared with k-means clustering algorithm, DBSCAN algorithm can automatically obtain the clustering number of complex shape data sets while detecting data noise points, which is more suitable for electric appliance data preprocessing of the smart electric appliance network.
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