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Enhancing Summer Electricity Demand Forecasting Using Fourier Transform-Based Time Variables

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(11), pp.31-40
  • DOI : 10.9708/jksci.2024.29.11.031
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 20, 2024
  • Accepted : October 24, 2024
  • Published : November 29, 2024

Jae-Ho Shin 1,  Hyun-Uk Seol 2,  Han-Byeol Jo 2,  Jong-Kwon Jo 2,  Sung-Ju Kim 3,  Byoung-Ho Jang 2,  Young-Soon Kim 1

1경상국립대학교
2(주)빅아이
3한국전기연구원

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ABSTRACT

In the summer, when the cooling load rises due to high temperatures, the hourly demand increases during the day and is relatively less at night compared to the day. These characteristics are considered important information in predicting summer electricity demand. However, if time information is simply expressed as a dummy variable, the model simply recognizes differences between time zones rather than learning changes in time. In this study, we would like to approach this problem by using a time variable using the Fourier transform. Time variables using the Fourier transform will be able to effectively learn differences between times. As a result of evaluating the type of time variable in the summer electricity demand forecast for 2022 and 2023 using the BiGRU model, the model using the time variable using Fourier transform showed the best performance with MAPE of 2.01% and 2.04% confirmed. The results of this study are expected to improve prediction accuracy in the summer when power usage increases and prevent problems such as large-scale power outages.

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This paper was written with support from the National Research Foundation of Korea.
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