Open AccessResearch on Electricity Information Acquisition System Based on Sample Data Mining Model
Author(s) -
Dang Zhongkui,
Lei Fu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1634/1/012093
Subject(s) - support vector machine , computer science , data mining , sample (material) , electricity , variety (cybernetics) , set (abstract data type) , data set , machine learning , scale (ratio) , power (physics) , artificial intelligence , engineering , chemistry , physics , chromatography , quantum mechanics , electrical engineering , programming language
In power user information collection and detection, power companies generally have a variety of different detection needs, or need to solve the problem while having additional requirements for certain aspects. Therefore, the SVM classification technology is used in the paper to carry out more detailed pattern recognition of power consumption characteristics for small-scale users or users with major suspicions. Moreover, given the imbalance of the abnormal electricity detection data set, a comprehensive processing model of unbalanced samples is constructed. Meanwhile, the differential evolution algorithm is applied to complete the SVM parameter optimization, which not only solves the problem that the SVM classification performance is significantly affected by the parameters, but also ensures the operating efficiency of the integrated classification model.