中圖分類(lèi)號(hào)： TN915,；TM933 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200073 中文引用格式： 段曉萌,，王爽，趙婷,，等. 基于FP-growth算法的用電異常數(shù)據(jù)挖掘方法[J].電子技術(shù)應(yīng)用,，2020，46(10)：47-50. 英文引用格式： Duan Xiaomeng,，Wang Shuang,，Zhao Ting，et al. Data mining method on abnormal electricity usage based on FP-growth algorithm[J]. Application of Electronic Technique,，2020,，46(10)：47-50.
Data mining method on abnormal electricity usage based on FP-growth algorithm
1.China Electric Power Research Institute，Beijing 100192,，China,； 2.State Grid Zhejiang Electric Power Co.，Ltd.,，Hangzhou 310007,，China
Abstract： Because of the technology development, the means for stealing electricity becomes more specialized and diversified. The traditional anti-theft technology is less real-time and less feasible. This paper studied the intelligent diagnosis and characteristics extract method of electricity energy meter during online operation, analyzed the abnormal electricity consumption data, used machine learning abnormality judgment thresholds based on features, and used association rule data mining methods to fuse independent detection results, realizing the mining of power theft data. At last, this paper verified the accuracy of the model establishment, and deduced the screening method of power consumption abnormal cases.
Key words : energy meter；abnormal electricity usage,；FP-growth algorithm,；data mining
