Estimação de consumo elétrico individual utilizando temporal convolutional network

• Ano de defesa: 2021
• Autor(a) principal: LEMOS, Victor Henrique Bezerra de
• Orientador(a): ALMEIDA, João Dallyson Sousa de
• Banca de defesa: ALMEIDA, João Dallyson Sousa de , PAIVA, Anselmo Cardoso de , BRAZ JUNIOR, Geraldo , OLIVEIRA, Ginalber Luiz de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO/CCET
• Departamento: DEPARTAMENTO DE INFORMÁTICA/CCET
• País: Brasil
• Palavras-chave em Português: Consumo elétrico; Séries temporais; Predição; Temporal convolutional network
• Palavras-chave em Inglês: Energy consumption; Forecasting; Time series; Temporal convolutional network
• Área do conhecimento CNPq: Ciência da Computação
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/3534

Every year, companies in the electricity distribution sector suffer losses due to problems in acquiring consumption data for billing. These problems range from human error to customer fraud. Thus, estimating monthly energy consumption is a problem of great interest in the context of electricity distribution companies, as a way of mitigating reading problems. A forecast that minimizes error plays an important role in identifying inconsistencies in the monthly billing process. For this, electric companies have invested in the use of prediction to define limits, between upper and lower, where a reading is considered normal. Thus, in this context, this work presents a method for predicting individual monthly electrical consumption. A method based on a Temporal Convolutional Networl (TCN) network was developed, combined with the application of an Optimization of the Hyperparameters of the proposed architecture. A pre-processing workflow was also created, able to alleviate some of the problems that can be found in the clients’ historical series, in addition to helping to generate a better representation of the time series for the predictive model. The proposed approach proposed SMAPE total 16.86 %, being superior in six of the eight consumer classes, which correspond to 98.23 % of the dataset, when compared to other methods found in the literature, such as: Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES),HOLT, Stocastic Gradient Descent (SGD), Long short term memory (LSTM) and the TCN network itself. In general, showing a proposed methodology capable of performing as expected, in the most different scenarios.