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Performance Assessment of Smart Electricity Meters Based on Max Margin Bayesian Classifiers

  • Haibo Yu
  • Helong Li
  • Yungang ZhuEmail author
  • Yang Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1084)

Abstract

There are large amount of quality and monitoring data of electricity meters, it is crucial and valuable to assess the operating performance of the smart electricity meters automatically with this data. In this paper, we propose an intelligent operating performance assessment method for smart electric meters based on selective ensemble of max margin Bayesian classifiers. The genetic algorithm is firstly used to select most relevant attributes for assessment, and max margin Bayesian classifiers is utilized to make the assessment. We use the bagging and clustering to ensemble multiple classifiers to obtain better results. The experimental results illustrate the efficiency and effectiveness of the proposed method.

Keywords

Ensemble learning Smart electricity meter Performance assessment 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.China Electric Power Research InstituteBeijingChina
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchunChina
  3. 3.China National Accreditation Service for Conformity AssessmentBeijingChina

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