@article{MD2EAA5FF,
title = "Electric Power Demand Prediction Using Deep Learning Model
with Temperature Data",
journal = "The Transactions of the Korea Information Processing Society",
year = "2022",
issn = "null",
doi = "https://doi.org/10.3745/KTSDE.2022.11.7.307",
author = "Hyoup-Sang Yoon/Seok-Bong Jeong",
keywords = "Electric Power Demand Prediction, Deep Learning, WaveNet, Temperature Data",
abstract = "Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast
techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based
prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose
a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction
accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a
delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input
data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified
WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%,
which is better than MAPE Value (2.33%) of the same model without temperature data."
}