Client: Leading Utility Organization

Problem Statement

Organization wanted to gauge value of its new Smart-Grid initiative for their infrastructure management:

• • Predicting meter events and outages including likely malfunction
  • Predicting spike in consumption to reduce spot-procurement
  • Ability to notify customers of their consumption habits / patterns in near-real time.

  Analysis Led Approach

  • Define population by zip codes, time period, manufacturer & events (power and meter events)
  • A pair wise correlation of events and Linear Regression model to predict total event count with variables as events identified in previous events
  • Prioritization Algorithms arrived by using cluster analysis for prediction of outages & spikes

  Business Approach

  • Prediction of last mile consumption spikes with accuracy of 89%
  • Increase up to 80% in prediction of consumption leakage
  • 10 times decrease costs decrease in network audit
  • Reduction in field staff workload that repairs/replaced smart meters at 0.014% from 1% of 1.2 million meters in a 45 day period

  Critical Success Factors

  • Consumption spikes accurately predicted with an efficiency of 89%
  • Consumption leakage controlled with a 80% positive prediction for similar events
  • Results seen in 45 days post implementation of the suggestions