The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Energy Demand/Supply Prediction and Simulator UI Design for Energy Efficiency in the Industrial Complex

산업단지 에너지 효율화를 위한 에너지 수요/공급 예측 및 시뮬레이터 UI 설계

  • Hyungah Lee ;
  • Jong-hyeok Park ;
  • Woojin Cho ;
  • Dongju Kim ;
  • Jae-hoi Gu (Institute for Advanced Engineering)
  • 이형아 (고등기술연구원 에너지환경IT융합그룹) ;
  • 박종혁 (고등기술연구원 에너지환경IT융합그룹) ;
  • 조우진 (고등기술연구원 에너지환경IT융합그룹) ;
  • 김동주 (고등기술연구원 에너지환경IT융합그룹) ;
  • 구재회 (고등기술연구원 에너지환경IT융합그룹)
  • Received : 2024.05.10
  • Accepted : 2024.06.15
  • Published : 2024.07.31
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Abstract

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

에너지 소비 문제가 전 세계적으로 주요한 이슈로 자리잡아 다양한 부문에서 에너지 소비 및 온실가스 배출 절감에 대한 관심이 크다. 2022년 3월 말 기준 국내 산업단지 총 면적은 606 km2로, 전체 국토면적의 약 0.6 %에 불과한다. 하지만 2018년 기준, 국내 산업단지의 연간 에너지 사용량은 국가 전체 에너지 사용량의 53.5 %, 전체 산업부문 에너지 사용량의 83.1 %를 차지하는 110,866.1천 TOE임으로 확인되었다. 더불어 국가 전체 온실가스 배출량의 45.1 %, 산업부문 온실가스 배출량의 76.8 %를 차지하여 환경에 미치고 있는 영향 또한 상당한 상황임이 확인하였다. 이러한 배경 하에 본 연구에서는 산업단지 차원의 에너지 효율화에 기여하고자, 국내 한 산업단지를 대상으로 에너지 수요 및 공급의 예측을 진행하였으며, 예측 결과값을 포함하여 에너지 모니터링을 위한 시뮬레이터 UI 화면을 설계하였다. 머신러닝 알고리즘 중 다층퍼셉트론 (Multi-Layer Perceptron; MLP)을 사용하였으며, 예측 모델의 최적화 기법으로서 베이지안 최적화 (Bayesian Optimization)를 적용하였다. 본 연구에서 구축한 예측 모델은 산업단지 내 압축공기 수요 유량의 경우는 87.90 %, 공용 공기압축기 공급 가능 유량의 경우는 99.54 %의 예측 정확도를 보였다.

Keywords

Acknowledgement

본 연구는 산업통상자원부(MOTIE)의 재원으로 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구입니다. (No.2020202900170)

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