Utilities – Revenue Loss Prevention

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A utility wanted to reduce the amount of revenue lost by identifying meters before they failed and being able to discern failing meters from other situations in which the meter data was outside the normal range.   In addition, they wanted to reduce the number of people dedicated to manually performing this task and move them into higher value activities.  The result was a solution that:


  • Predicts current usage based on historical data
  • Monitors when actual usage differs from predicted usage
  • Classifies meters into performance categories: normal, over-performing, under-performing, not performing
  • Generate exception reports
  • Mines and identifies usage patterns and trends


Technologies: SQL Server, Azure Cloud Service, Cortana Analytics Machine Learning   Industry: Utilities

Our Work Included:

  • Developing an analytical model
  • Crafting a dashboard with meaningful visualizations
  • Integrating external data with meter data
  • Creating an intuitive front end for easy data input and accessing results

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