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LSTM-based Power Load Prediction System Design for Store Energy Saving

매장 에너지 절감을 위한 LSTM 기반의 전력부하 예측 시스템 설계

  • Choi, Jongseok (Spartan Software Education Institute, Soongsil University) ;
  • Shin, Yongtae (School of Computing, Soongsil University)
  • Received : 2021.08.13
  • Accepted : 2021.08.21
  • Published : 2021.08.30

Abstract

Most of the stores of small business owners are those that use a large number of electrical devices, and in particular, there are many stores that use a cold storage system. In severe cases, there is a lot of power load on the store, which can cause a loss to the assets in the store as the power supply is cut off. Accordingly, in this paper, an LSTM-based power load prediction system was designed to measure the energy demand rate of stores and to save energy. Since it can be used as a data-based power saving system for small and medium-sized stores, it is expected to be used as a data-based power demand prediction system for small businesses in the future, and to be used in the field of preventing damage due to power load.

소상공인 업체들의 매장은 다수의 전기기기를 사용하는 매장들이 대부분이며 특히 냉장 시스템을 이용한 매장이 많아 여름, 겨울의 계절 변화에 따라 전력의 수요가 변화하고 온도의 급변에 냉장 시스템을 적용시키지 못할 시에 많은 전력부하가 발생되어 심할 경우 전력공급의 차단이 발생됨에 따라 매장 내 자산에 손실을 미칠 수 있다. 이에 따라 본 논문에서는 매장의 에너지 수요율을 측정하고 에너지를 절감하기 위하여 LSTM 기반의 전력 부하 예측 시스템을 설계하였다. 이는 데이터 기반의 중소 매장용 전력절감 시스템으로 사용될 수 있어 향후 소상공인 데이터 기반의 전력 수요 예측 시스템으로 사용되고, 전력 부하로 인한 피해 방지 분야에서 사용될 것으로 예상된다.

Keywords

References

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