푸리에 변환 기반 시간변수를 이용한 하계 전력수요 예측
Enhancing Summer Electricity Demand Forecasting Using Fourier Transform-Based Time Variables
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초록
높은 기온으로 인해 냉방부하가 상승하는 하절기의 낮에는 시간당 전력 수요가 증가하고 밤에는 낮에 비해 상대적으로 전력수요가 적은 특징을 보인다. 이러한 특징은 하계 전력 수요 예측에있어 중요한 정보로 간주한다. 그러나 시간 정보를 단순히 더미변수로 표현할 경우, 모델은 시간의 변화를 학습하기보다는 단순히 시간대 간의 차이를 인식하는 데 그치게 된다. 본 연구에서는이러한 문제를 해결하기 위해 푸리에 변환을 활용하여 시간 변수를 사용한 방식으로 접근하고자한다. 푸리에 변환을 이용한 시간 변수는 시간 간의 차이를 효과적으로 학습할 수 있을 것이다. BiGRU 모델을 사용하여 2022년과 2023년의 하계 전력 수요예측에 시간 변수의 형식에 따른 평가를 수행한 결과 푸리에 변환을 사용한 시간 변수를 사용한 모델이 MAPE 2.01%, 2.04%로 가장 우수한 성능을 보이는 것을 확인하였다. 본 연구의 결과는 전력 사용량이 증가하는 하절기의 전력사용량 예측 정확도를 높여, 대규모 정전 사태와 같은 문제를 방지할 수 있을 것으로 기대한다.
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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