Inspection Intelligence for Energy Recovery

The context

In a context characterized by continuous energy losses and fragmented control processes, the project was created to respond to the need of a customer in the energy sector to optimize field inspection activities, increase operational efficiency and proactively combat unauthorized connections to the electrical distribution network.

THE CHALLENGE

The objectives of the project

The main objectives of the project were:

  • Achieve a level of energy loss contained within recognized limits.

  • Increase the productivity of field inspections through the use of predictive and geoanalytical data.

  • Automate low-value-added back-office activities related to energy recovery.

  • Use external sources, such as satellite images, to promptly identify irregular connections to the electrical grid.

Platforms and technologies:

Platforms and technologies:

The solution was built using a modern and scalable architecture based on:

  • Languages and frameworks: Python, Spring Boot, AngularJS, Scala

  • AWS cloud and services: Amazon Web Services (AWS), Amazon S3, Amazon Aurora, Amazon Redshift

  • Big data and architectures: Apache Spark, MicroServices, GIS

The Target Reply Roma solution

Target Reply Rome has designed and developed an end-to-end web solution to address these challenges. The implemented platform allows end users to:

  • View and manage: Monitor fraudulent models reported by built-in predictive artificial intelligence models.

  • Automate: automatically manages the monitoring of inspection results and back-office activities for energy recovery.

  • Plan: integrate reports, complaints, and model results to compose optimized field campaigns and generate inspection orders directly from the user interface.

  • Innovate: it incorporates external data (structured and not) into predictive models, uses satellite images to detect illegal users and automatically calculate the energy to be recovered, also implementing assisted negotiation modules.

It is estimated that there will be a 15% increase in the productivity of field inspections and a 30% savings in back-office activities by optimizing processes and reducing losses.