Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

Marcus Vinícius Coelho Vieira da Costa, Osmar Luiz Ferreira de Carvalho, Alex Gois Orlandi, Issao Hirata, Anesmar Olino de Albuquerque, Felipe Vilarinho e Silva, Renato Fontes Guimarães, Roberto Arnaldo Trancoso Gomes and Osmar Abílio de Carvalho Júnior
Additional contact information
Marcus Vinícius Coelho Vieira da Costa: Superintendency of Information Technology, Brazilian Electricity Regulatory Agency, Brasília 70.910-900, Brazil
Osmar Luiz Ferreira de Carvalho: Department of Computer Science, University of Brasília, Brasília 70.910-900, Brazil
Alex Gois Orlandi: Superintendency of Information Technology, Brazilian Electricity Regulatory Agency, Brasília 70.910-900, Brazil
Issao Hirata: Superintendency of Information Technology, Brazilian Electricity Regulatory Agency, Brasília 70.910-900, Brazil
Anesmar Olino de Albuquerque: Department of Geography, University of Brasília, Brasília 70.910-900, Brazil
Felipe Vilarinho e Silva: Superintendency of Information Technology, Brazilian Electricity Regulatory Agency, Brasília 70.910-900, Brazil
Renato Fontes Guimarães: Department of Geography, University of Brasília, Brasília 70.910-900, Brazil
Roberto Arnaldo Trancoso Gomes: Department of Geography, University of Brasília, Brasília 70.910-900, Brazil
Osmar Abílio de Carvalho Júnior: Department of Geography, University of Brasília, Brasília 70.910-900, Brazil

Energies, 2021, vol. 14, issue 10, 1-15

Abstract: Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach.

Keywords: solar panel; deep learning; semantic segmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/10/2960/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/10/2960/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:10:p:2960-:d:558495

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().