Estimação de consumo elétrico individual utilizando temporal convolutional network
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 dist...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | Brasil |
| Institución: | Universidade Federal do Maranhão (UFMA) |
| Repositorio: | Biblioteca Digital de Teses e Dissertações da UFMA |
| Idioma: | portugués |
| OAI Identifier: | oai:tede2:tede/3534 |
| Acceso en línea: | https://tedebc.ufma.br/jspui/handle/tede/3534 |
| Access Level: | acceso abierto |
| Palabra clave: | Consumo elétrico Séries temporais Predição Temporal convolutional network Energy consumption Forecasting Time series Ciência da Computação |
| Sumario: | 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. |
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