NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning.

Autor: Gremes, Matheus Felipe, Gomes, Renato Couto, Heberle, Andressa Ullmann Duarte, Bergmann, Matheus Alan, Ribeiro, Luísa Treptow, Adamski, Janice, dos Santos, Flávio Alves, Moreira, André Vinicius Rodrigues, Lameirão, Antonio Manoel Matta dos Santos, de Toledo, Roberto Farias, de C. Filho, Antonio Oseas, Andrade, Cid Marcos Gonçalves, Lima, Oswaldo Curty da Motta
Předmět:
Zdroj: Sensors (14248220); Aug2024, Vol. 24 Issue 15, p4924, 23p
Abstrakt: This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company's coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje