Transactions of the Korean hydrogen and new energy society (한국수소및신에너지학회논문집)

The Korean Hydrogen and New Energy Society (한국수소및신에너지학회)
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Deep Learning Based Electricity Demand Prediction and Power Grid Operation according to Urbanization Rate and Industrial Differences

도시화율 및 산업 구성 차이에 따른 딥러닝 기반 전력 수요 변동 예측 및 전력망 운영

  • KIM, KAYOUNG (Department of Climate and Energy Systems Engineering, Ewha Womans University) ;
  • LEE, SANGHUN (Department of Climate and Energy Systems Engineering, Ewha Womans University)
  • 김가영 (이화여자대학교 엘텍공과대학 기후.에너지시스템공학전공) ;
  • 이상훈 (이화여자대학교 엘텍공과대학 기후.에너지시스템공학전공)
  • Received : 2022.08.30
  • Accepted : 2022.10.15
  • Published : 2022.10.30
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Abstract

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

Keywords

Acknowledgement

이 연구는 2022학년도 이화여자대학교 교내연구비 지원에 의한 연구입니다. 이 논문은 정부(기상청)의 재원으로 한국기상산업기술원의 기상기후데이터융합분석 특성화대학원 사업의 지원을 받아 수행되었습니다.

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