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주거 커뮤니티 전력 수요 예측을 위한 단계별 확률적 통계 모델 개발
김철호(Kim, Chulho),변지욱(Byun, Jiwook),고재현(Go, Jaehyun),허연숙(Heo, Yeonsook) 대한건축학회 2021 대한건축학회논문집 Vol.37 No.7
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.
주거 커뮤니티 급탕 수요 예측을 위한 확률적 통계 모델 개발
김철호(Kim, Chulho),변지욱(Byun, Jiwook),고재현(Go, Jaehyun),허연숙(Heo, Yeonsook) 대한건축학회 2021 대한건축학회 학술발표대회 논문집 Vol.41 No.2
This study developed a series of probabilistic statistical models for domestic hot water demand prediction in residential communities. The series of probabilistic models were developed to reflect individual variation in the domestic hot water demand depending on household characteristics and temporal variability in the pattern of hourly domestic hot water use in a systematic manner. We used the hourly domestic hot water data from 15 households selected from the public data of the Korea Energy Agency. Models 1 and 2 are based on linear regression models to predict the annual average domestic hot water load depending on the household characteristics and variation in the daily domestic hot water load, respectively. Models 3 and 4 are based on the multivariate normal distribution and beta distribution probability density function to generate hourly domestic hot water load profiles reflecting temporal variation. As a result of applying probabilistic domestic hot water load profiles, individual and temporal variations were reflected.
주거 커뮤니티 전력 수요 예측을 위한 확률적 통계 모델 개발
김철호(Kim, Chulho),변지욱(Byun, Jiwook),고재현(Go, Jaehyun),허연숙(Heo, Yeonsook) 대한건축학회 2021 대한건축학회 학술발표대회 논문집 Vol.41 No.1
This study developed a series of probabilistic statistical models for electricity demand prediction in residential communities. The series of probabilistic models were developed to reflect individual variation in the electricity demand depending on household characteristics and temporal variability in the pattern of hourly electricity use in a systematic manner. We used the hourly electricity data, including plug-in and lighting energy, from 23 households selected from the public data of the Korea Energy Agency. Models 1 and 2 are based on linear regression models to predict the annual average electricity load depending on the household characteristics and variation in the daily electricity load, respectively. Models 3 and 4 are based on the multivariate normal distribution probability density function to generate hourly electricity load profiles reflecting temporal variation. As a result of applying probabilistic electricity load profiles, individual and temporal variations were reflected.
교육 시설의 전력 수요 예측을 위한 클러스터링 기반 확률적 통계 모델 개발
김철호(Chulho Kim),김한주(Hanjoo Kim),변지욱(Jiwook Byun),고재현(Jaehyun Go),허연숙(Yeonsook Heo) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
This study developed the probabilistic models of the internal electricity load for educational buildings on the basis of the structured probabilistisic model framework developed in the previous study for residential buildings. In this process, heterogeneous public data sosurces (MOLIT EAIS and KEPCO AMI) were used, and differences in internal electricity load within education buildings were invetigated with using the clustering method. The cluster analysis revealed two distinctive load profiles, which may be due to different types of educational purposes: schools and private educational institutes. Within each cluster of buildings, individual-building variations of internal electricity load, as well as temporal variations of internal electricity load were quantified by the structured probabilistic models Statistical models 1 and 3 can represent individual variations in internal electricity load for each building, and statistical models 2 and 4 can reflect temporal variations in hourly internal electricity load patterns that change daily.
건축물 에너지 수요 예측을 위한 이종 공공 데이터 통합 방안 연구
김철호(Chulho Kim),김한주(Hanjoo Kim),변지욱(Jiwook Byun),고재현(Jaehyun Go),허연숙(Yeonsook Heo) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
This study investigated a method of integrating building energy-consumption public data such as MOLIT EAIS and KEPCO AMI to develop the probabilistic models to predict the electricity demand of non-residential buildings. The data set was created by integrating electricity data composed by consideration of time (24 hours) of 9 business types into 5 building types (i.e., education, office, hotel, culture, retail). Individual building energy data source alone provides partial information, and different data sources are at different temporal resolution. Therefore, there is a strong need to develop a framework to integrate various types of public data sets, and this data-integration framework will be essential to develop building energy forecasting models at high resolution levels.
건축물 에너지 수요 예측을 위한 이종 공공 데이터 통합 방안 연구
김철호(Chulho Kim),김한주(Hanjoo Kim),변지욱(Jiwook Byun),고재현(Jaehyun Go),허연숙(Yeonsook Heo) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
This study investigated a method of integrating building energy-consumption public data such as MOLIT EAIS and KEPCO AMI to develop the probabilistic models to predict the electricity demand of non-residential buildings. The data set was created by integrating electricity data composed by consideration of time (24 hours) of 9 business types into 5 building types (i.e., education, office, hotel, culture, retail). Individual building energy data source alone provides partial information, and different data sources are at different temporal resolution. Therefore, there is a strong need to develop a framework to integrate various types of public data sets, and this data-integration framework will be essential to develop building energy forecasting models at high resolution levels.