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EnergyWatch

The challenge

Energy distributors lose billions of reais every year due to fraud and non-technical losses.

Traditional detection methods, based on traditional statistics, unsystematic expert knowledge, or random inspections, are ineffective:

Field inspections are expensive and time-consuming.
The success rate is low, as many visits are made to consumer units without irregularities.
This results in wasted resources, high operational costs, and low effectiveness in revenue recovery.

The solution

EnergyWatch uses data analytics and artificial intelligence to identify fraud patterns and prioritize inspection targets with the highest probability of irregularities and the greatest potential for energy recovery.

The platform analyzes large volumes of consumption data, customer history, network variables, and images, generating predictive models that classify and rank suspicious consumers.

This allows field teams to be directed only to locations and consumer units with the highest probability of high fraud volumes, increasing operational efficiency, inspection success rates, and revenue recovery for distributors.

Results obtained

In the Rio de Janeiro metropolitan area, EnergyWatch achieved:

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Hit Rate: 31.2%, representing a 28% increase compared to the traditional method.

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Average TOI of 4,289 kWh, with a gain of 120%.

In another application case, the solution presented:

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Hit Rate

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Average TOI of 4,289 kWh, with a gain of 120%.

Benefits

  • Intelligent inspection prioritization, based on AI and advanced data analysis.

  • Greater energy recovered and reduced energy required

  • Higher success rate in fraud detection.

  • Optimization of field resources, focusing on more relevant targets.

  • Reduction of operating costs.

  • Scalability, applicable to millions of consumer units.

  • Data-driven decision making with clear and reliable reports.

  • Internalization, systematization and standardization of the knowledge of loss specialists

  • Less dependence on dynamic and/or ineffective business rules

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