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This article explores how Business Intelligence (BI) can be applied to analyze and prevent technical failures in an electric utility’s electric meters. Efficient meter management is crucial to ensure accurate metering of electricity consumption and early detection of potential problems. Using BI techniques, we can improve operational efficiency and reduce costs by identifying anomalous patterns and predicting faults before they affect electric service. The research used qualitative probabilistic non-inferential methodology for data collection and the Hephaestus methodology for the analysis of 7014 records of the year 2022 of the Santa Elena electric company. In addition, a dashboard was designed with the Power BI tool that allowed visualizing statistical data on the technical reviews performed by the meter laboratory staff showing accurate and reliable results on meter failures, providing advanced data analysis, monitoring, and predictive analysis to improve operational efficiency and decision making in response to the findings.

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Business Intelligence for Fault Detection and Diagnosis in Electrical Meters

  • 会议论文

  • 发表日期：

    2025年

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  • Melissa Cecilia Borbor Villón,
  • Delia Isabel Carrión León,
  • Evelin Betzabeth Banchón Ramos,
  • Arcesio Franklin Bustos Gaibor

摘要

This article explores how Business Intelligence (BI) can be applied to analyze and prevent technical failures in an electric utility’s electric meters. Efficient meter management is crucial to ensure accurate metering of electricity consumption and early detection of potential problems. Using BI techniques, we can improve operational efficiency and reduce costs by identifying anomalous patterns and predicting faults before they affect electric service. The research used qualitative probabilistic non-inferential methodology for data collection and the Hephaestus methodology for the analysis of 7014 records of the year 2022 of the Santa Elena electric company. In addition, a dashboard was designed with the Power BI tool that allowed visualizing statistical data on the technical reviews performed by the meter laboratory staff showing accurate and reliable results on meter failures, providing advanced data analysis, monitoring, and predictive analysis to improve operational efficiency and decision making in response to the findings.