Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Development of Data Visualized Web System for Virtual Power Forecasting based on Open Sources based Location Services using Deep Learning

오픈소스 기반 지도 서비스를 이용한 딥러닝 실시간 가상 전력수요 예측 가시화 웹 시스템

  • Lee, JeongHwi (Department of Computer Science, Sangmyung University) ;
  • Kim, Dong Keun (Department of Human-Centered Artificial Intelligence, Sangmyung University)
  • Received : 2021.03.18
  • Accepted : 2021.07.23
  • Published : 2021.08.31
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Abstract

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

최근 웹에서 지도(Map)를 이용한 Location based Services 기반의 다양한 위치정보시스템 활용이 점점 확대되고 있으며 에너지 절약을 위한 대안으로 전력 수요 현황을 실시간으로 확인할 수 있는 모니터링 시스템의 필요성이 요구되고 있다. 본 연구에서는 딥러닝과 같은 기계학습을 이용하여 전력 수요 데이터의 특성을 분석하고 예측하는 모듈을 개발하여 지역 단위별 전력 에너지 사용 현황과 예측 추세를 실시간으로 확인할 수 있는 오픈소스 기반 지도 서비스를 이용한 딥러닝 실시간 가상 전력수요예측 웹 시스템을 개발하였다. 특히 제안한 시스템은 LSTM 딥러닝 모델을 이용하여 지역적으로 전력 수요량과 예측 분석이 실시간으로 가능하고 분석된 정보를 가시화하여 제공한다. 향후 제안된 시스템을 통해 지역별 에너지의 수급 및 예측 현황을 확인하고 분석하는데 활용될 수 있을 뿐만 아니라 다른 산업 에너지에도 적용될 수 있을 것이다.

Keywords

Acknowledgement

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No.2018201060010C).

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