@InProceedings{RochaVPBAMSRSMN:2021:CoStMe,
author = "Rocha, Carlos Vinicios Martins and Vieira, Pedro Henrique Carvalho
and Pinto, Antonio Moreira and Bernhard, Pedro Vinnicius and
Anchieta Junior, Ricardo Jos{\'e} Fernandes and Marques, Ricardo
Costa da Silva and Silva, Italo Francyles Santos da and Rocha,
Simara Vieira da and Silva, Arist{\'o}fanes Corr{\^e}a and
Monteiro, Eliana M{\'a}rcia Garros and Nogueira, Hugo Daniel
Castro Silva",
affiliation = "{Federal University of Maranh{\~a}o} and {Federal University of
Maranh{\~a}o} and {Federal University of Maranh{\~a}o} and
{Federal University of Maranh{\~a}o} and {Federal University of
Maranh{\~a}o} and {Federal University of Maranh{\~a}o} and
{Federal University of Maranh{\~a}o} and {Federal University of
Maranh{\~a}o} and {Federal University of Maranh{\~a}o} and
{Equatorial Energy Group} and {Equatorial Energy Group}",
title = "A Comparative Study of Methods based on Deep Neural Networks for
Self-reading of Energy Consumption in a Chatbot Application
Context",
booktitle = "Proceedings...",
year = "2021",
editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and
Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario
and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos,
Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira,
Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir
A. and Fernandes, Leandro A. F. and Avila, Sandra",
organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "self-reading, energy consumption, chatbot application, image
processing, deep learning.",
abstract = "Self-reading is a process in which the consumer is responsible for
measuring his own energy consumption, which can be done through
digital platforms, such as websites or mobile applications. The
Equatorial Energy group's electric utilities have been working on
developing a chatbot application through which consumers can send
an image of their energy meter to a server that runs a method
based on image processing and deep learning for the automatic
recognition of consumption reading. However, the incorporation of
these methods in a solution available to the public should
consider factors such as response time and accuracy, so that it
presents a satisfactory response time when it needs to handle a
large number of simultaneous requests. Therefore, this paper
presents a comparative study between approaches developed for the
automatic recognition of consumption readings in images of
electric meters sent to the server. Response time performances are
analyzed through stress tests that simulate the real application
scenario. The mean average precision (mAP) and the accuracy
metrics of the methods are also analyzed in order to evaluate the
generalization of the used convolutional neural networks.",
conference-location = "Gramado, RS, Brazil (virtual)",
conference-year = "18-22 Oct. 2021",
language = "en",
ibi = "8JMKD3MGPEW34M/45DJ23H",
url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45DJ23H",
targetfile = "Autoclara___Sibgrapi_2021___English__sem_subpastas_.pdf",
urlaccessdate = "2024, May 03"
}