학술논문
Temporal Convolutional Network applied for Forecasting Individual Monthly Electric Energy Consumption
Document Type
Conference
Author
Source
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2020 IEEE International Conference on. :2002-2007 Oct, 2020
Subject
Language
ISSN
2577-1655
Abstract
The task of predicting energy consumption is a problem of great interest in electric power companies. A minimal error prediction is essential for identifying inconsistencies in the monthly consumption reading process. This paper presents a methodology applied to electric consumption prediction and was performed with and without a hyperparameter optimization strategy using a TCN network. We apply these strategies to indi-vidual electric consumption time series. The TCN approach had superior results when compared to SES, ARIMA, and Gradient Boosting. The results show that the proposed process obtained low efficiency with approximately 1% or less improvement than the use of no optimization. However, the TCN itself showed promising results being the best approach in many of our tests.