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As companhias do setor elétrico têm apresentado a autoleitura como uma alternativa eficiente para mitigar perdas financeiras e seguir as medidas sanitárias recomendadas em virtude da pandemia do Covid-19. Nesse contexto, este trabalho apresenta uma solução de chatbot para a autoleitura por meio de aplicativos de mensagens. O chatbot é integrado a um método que aplica processamento de imagem e inteligência computacional para a leitura automática do consumo de energia. Essa solução encontra-se em desenvolvimento no âmbito de um projeto de pesquisa e desenvolvimento (P&D). Preliminarmente, o método de reconhecimento das leituras apresenta acurácia de 89% e 77,2% para os medidores analógicos e digitais, respectivamente.

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.