Authors:
Álisson Alves
1
;
2
;
Luísa Souza
2
;
1
;
Luiz Cho-Luck
2
;
Raniere Lima
2
;
Carlos Augusto
2
;
Wesley Marinho
2
;
1
;
Rafael Capuano
2
;
Bruno Costa
2
;
Marina Siqueira
2
;
Jesaías Silva
2
;
Raul Paradeda
3
and
Pablo Alsina
1
Affiliations:
1
Graduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal-RN, Brazil
;
2
SENAI Institute of Innovation in Renewable Energy, Capitão-Mor Gouveia Avenue, Natal-RN, Brazil
;
3
Department of Computer Science, State University of Rio Grande do Norte, Dr. João Medeiros Filho Avenue, Natal-RN, Brazil
Keyword(s):
Electrical Infrastructure, Semantic Segmentation, Deep Learning, Remote Sensing, Geospatial Analysis, Land Use Classification, Infrastructure Mapping.
Abstract:
Managing urban expansion and its impact on electrical infrastructure presents significant challenges, necessitating innovative methodologies to address irregular settlements and commercial losses in the electricity sector. This paper proposes an approach integrating convolutional neural networks and geospatial data to detect urban areas lacking electrical infrastructure. High-resolution Google Earth images and low-resolution Landsat 8 data were processed using advanced semantic segmentation architectures, LinkNetB7 and D-LinkNet50, to analyze land use patterns. The segmentation outputs were combined with data from the Brazilian Geographic Database of the Distribution System to generate comprehensive maps of electrical infrastructure coverage. The study focused on the SBAU substation in Sabaŕ a, Minas Gerais, which demonstrated commercial losses of up to 47.5% in specific feeders. Results demonstrated the effectiveness of deep learning models in identifying mismatches between urban de
velopment and infrastructure coverage, highlighting areas with potential irregular connections. This study contributes to advancing artificial intelligence applications in urban energy management by providing a scalable framework for analyzing land use and electrical infrastructure.
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