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Development of the Series of Probabilistic Statistical Models for Electricity Demand Prediction in Residential Communities

주거 커뮤니티 전력 수요 예측을 위한 단계별 확률적 통계 모델 개발

  • 김철호 (고려대 미래건설환경융합연구소) ;
  • 변지욱 (고려대 대학원 건축사회환경공학과) ;
  • 고재현 (고려대 대학원 건축사회환경공학과) ;
  • 허연숙 (고려대 건축사회환경공학과)
  • Received : 2021.05.27
  • Accepted : 2021.07.09
  • Published : 2021.07.30

Abstract

This study developed a series of probabilistic statistical models for electricity demand prediction of residential communities. The series of probabilistic models were developed to reflect individual variations in the electricity demand depending on household characteristics and temporal variability in the pattern of hourly electricity use. We used the hourly electricity data, including plug-in and lighting energy use, from 23 households selected from the public data of the Korea Energy Agency. The prediction model consists of four models to capture variability in the electiricity demand at different indiviual and time scales. Models 1 and 2 are blinear regression models that predict the annual average electricity load depending on the household characteristics and variation in the daily electricity load, respectively. Models 3 and 4 are multivariate normal distribution probability density functions that generate average hourly electricity load profile and temporal variations from the average profile, respectively. The results demonstrarate that the series of probabilistic models sufficiently reflect actual individual and temporal variations.

Keywords

Acknowledgement

본 연구는 국토교통부 / 국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 KAIA21HSCT-B157919-02).

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