Electric Power Demand Prediction Using Deep Learning Model with Temperature Data


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 7, pp. 307-314, Jul. 2022
https://doi.org/10.3745/KTSDE.2022.11.7.307,   PDF Download:
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.


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Cite this article
[IEEE Style]
H. Yoon and S. Jeong, "Electric Power Demand Prediction Using Deep Learning Model with Temperature Data," KIPS Transactions on Software and Data Engineering, vol. 11, no. 7, pp. 307-314, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.7.307.

[ACM Style]
Hyoup-Sang Yoon and Seok-Bong Jeong. 2022. Electric Power Demand Prediction Using Deep Learning Model with Temperature Data. KIPS Transactions on Software and Data Engineering, 11, 7, (2022), 307-314. DOI: https://doi.org/10.3745/KTSDE.2022.11.7.307.