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NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2

Matheus Felipe Gremes1, Renato Couto Gomes2, Andressa Ullmann Duarte Heberle2

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This study presents an orbital monitoring system to detect non-technical losses (NTLs) in electricity grids using satellite data and AI. The system accurately identifies urban areas, improving the detection of potential energy theft and infrastructure issues.

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Area of Science:

  • Electrical Engineering
  • Remote Sensing
  • Computer Science

Background:

  • Non-technical losses (NTLs) in electricity distribution networks pose significant economic challenges.
  • Accurate identification of NTLs is crucial for grid efficiency and revenue protection.
  • Existing methods for NTL detection often lack precision and scalability.

Purpose of the Study:

  • To develop and validate an orbital monitoring system for quantifying non-technical losses (NTLs) in electricity distribution networks.
  • To leverage satellite imagery and advanced AI techniques for precise urban area segmentation.
  • To enhance the reliability of identifying areas with potential electricity theft or infrastructure irregularities.

Main Methods:

  • Utilized Sentinel-2 satellite imagery for data acquisition.
  • Employed computer vision and machine learning for accurate urban area segmentation.
  • Incorporated cloud removal techniques and OpenStreetMap masks for pre-annotation.
  • Tested the system on diverse datasets from France and Brazil.

Main Results:

  • Achieved a Jaccard index (IoU) of 0.9210 on the training dataset (France).
  • Attained a Jaccard index (IoU) of 0.88 on the test dataset (Brazil).
  • Demonstrated high efficacy and resilience in urban area segmentation across different regions.

Conclusions:

  • The developed orbital monitoring system effectively quantifies NTLs by precisely segmenting urban zones.
  • The system reliably identifies areas outside of utility coverage, indicating potential irregularities.
  • This approach offers a promising solution for mitigating NTLs and improving electricity grid management.