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A Chatbot Solution for Self-Reading Energy Consumption via Chatting Applications

Authors
  • Rocha, Carlos Vinicios Martins1
  • Lima, Arthur Azevedo1
  • Vieira, Pedro Henrique Carvalho1
  • Cipriano, Carolina Lima Saraiva1
  • Paiva, Anderson Matheus Passos1
  • da Silva, Italo Francyles Santos1
  • da Rocha, Simara Vieira1
  • Silva, Aristófanes Corrêa1
  • Nogueira, Hugo Daniel Castro Silva2
  • Monteiro, Eliana Márcia Garros2
  • Fernandes, Eduardo Camacho2
  • 1 Federal University of Maranhão,
  • 2 Equatorial Energy Group, São Luís, Brazil
Type
Published Article
Journal
Journal of Control, Automation and Electrical Systems
Publisher
Springer US
Publication Date
Sep 26, 2021
Pages
1–12
Identifiers
DOI: 10.1007/s40313-021-00818-6
PMCID: PMC8475340
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

To mitigate financial loss and follow the recommended sanitary measures due to the COVID-19 pandemic, self-reading, a method in which a consumer reads and reports his own energy consumption, has been presented as an efficient alternative for power companies. In such context, this work presents a solution for self-reading via chatbot in chatting applications. This solution is under development as part of a research and development (R&D) project. It is integrated with a method based on image processing that automatically reads the energy consumption and recognizes the identification code of a meter for validation purposes. Furthermore, all processes utilize cognitive services from the IBM Watson platform to recognize intentions in the dialog with the consumers. The dataset used to validate the proposed method for self-reading contains examples of analogical and digital meters used by Equatorial Energy group. Preliminary results presented accuracies of 77.20% and 84.30%, respectively, for the recognition of complete reading sequences and identification codes in digital meters and accuracies of 89% and 95.20% in the context of analogical meters. Considering both meter types, the method obtains an accuracy per digit of 97%. The proposed method was also evaluated with UFPR-AMR public dataset and achieves a result comparable to the state of the art.

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