Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Analysis of Apartment Power Consumption and Forecast of Power Consumption Based on Deep Learning

공동주택 전력 소비 데이터 분석 및 딥러닝을 사용한 전력 소비 예측

  • Yoo, Namjo (Dept. of Electronics Engineering, Hankuk University of Foreign Studies) ;
  • Lee, Eunae (Dept. of Electronics Engineering, Hankuk University of Foreign Studies) ;
  • Chung, Beom Jin (Dept. of Electrical and Information Engineering, Seoul National University of Science and Technology) ;
  • Kim, Dong Sik (Dept. of Electronics Engineering, Hankuk University of Foreign Studies)
  • Received : 2019.12.02
  • Accepted : 2019.12.29
  • Published : 2019.12.31
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Abstract

In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

에너지의 생산 효율성을 증가시키기 위해 최근 스마트그리드 기술 중 지능형 검침 시스템(AMI, advanced metering infrastructure)의 개발이 활발히 진행되고 있다. 전력 소비 데이터를 분석하고 소비 패턴을 예측하는 일은 AMI에서 핵심적인 부분이다. 본 논문에서는 수집된 전력 소비 데이터를 분석하고 발생할 수 있는 오류들을 정리하였으며 소비 패턴을 월별로 k-means 군집화 알고리즘을 사용하여 분석하였다. 또한 deep neural network를 이용하여 소비 패턴을 예측하였는데, 가구별 하루 전력 사용량 예측의 어려움을 극복하기 위하여 전력 사용량을 100개의 군집으로 분류하여 이 군집의 하루 평균으로 다음날 군집의 평균을 예측하였다. 실제 AMI에서의 전력 데이터를 사용하여 오류들을 분석하였으며 군집화 방법을 도입하여 성공적으로 전력 소비 예측이 가능하였다.

Keywords

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