Data Cleaning Method Based on Time Series Similarity Measurement for Large Scale Smart Grid Load Data

被引：7
|
作者：
Lei, Yu [1 ]
Lin, RongHeng [1 ]
Zou, Hua [1 ]
Zhou, Shiqi [1 ]
Zhang, Yong [2 ]
机构：
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China
[2] State Grid Shanghai Municipal Elect Power Co, Shanghai, Peoples R China
来源：
2017 5TH INTERNATIONAL CONFERENCE ON ENTERPRISE SYSTEMS (ES) | 2017年
基金：
北京市自然科学基金;
关键词：
smart grid; load data; time series; similarity measurement; data cleaning;
D O I：
10.1109/ES.2017.8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
On account of the strong time series feature of smart grid load data, this paper presents a data cleaning method based on similarity measurement of time series, which detects the abnormal data of load data and fills the vacancy value. In this paper, an up-and-coming symbolic method symbolic aggregate approximation(SAX) is applied to the similarity study of 96-point load data. The Euclidean distance algorithm is used to measure the similarity of time series, and the load data are cleaned according to the fitted curves obtained by adjusting similar sequences weighted by similarity. The experimental results show that the method has adequate accuracy and low computational complexity.
引用
收藏
页码：7 / 12
页数：6
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