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건물 에너지 소비 시스템에서 이상 탐지 성능향상을 위한 그룹 기반 변수 선택 방법
정다현(Dahyun Jung),전창재(Chang-Jae Chun) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Since buildings occupy a large part of the worlds energy consumption and CO₂ emissions, it is necessary to improve the building energy consumption efficiency to solve energy demands and environmental problems. Therefore, many studies have been conducted in the field of energy consumption anomaly detection in order to reduce energy waste in buildings and operate buildings more efficiently. However, many conventional studies have used different features to detect anomalies at the discretion of the researchers. This is because there are a wide range of features that may affect energy consumption and there is no clear standard or methods to effectively select them.. In this research, the feature variables used for anomaly detection of building energy consumption data are classified into 6 groups and the performance was compared when a combination of different groups was used as a model input feature variable.
토공사 건설공정계획 지원을 위한 이산형 건설시뮬레이션과 인공신경망 기반 생산성 예측 방법론 개발
정다현 ( Jung Dahyun ),임소민 ( Im Somin ),오정환 ( Oh Jeonghwan ),이재우 ( Lee Jaewoo ),한승우 ( Han Seungwoo ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.1
Construction operation planning based on productivity analysis is essential for successful construction management in the construction industry. Up to this date, however, productivity analysis is not yet conducted with accurate data. Therefore, this study analyzes productivity depending on combinations of equipment and state of roads that dump trucks travel on, using construction simulation based on data collected in actual earthmoving construction sites, and develops methodology of predicting productivity for construction sites with varying conditions using Artificial Neural Network(ANN) model.
토공사 공정관리를 위한 이산형 건설시뮬레이션과 인공신경망 기반 건설성능지표 도출 방법론
정다현 ( Jung¸ Dahyun ),박성봉 ( Park¸ Seongbong ),이수민 ( Lee¸ Sumin ),한승우 ( Han¸ Seungwoo ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.2
Demands for digital transformation of the construction industry are increasing to improve the accuracy of the construction operation planning and the performance of the construction operation. Even though large number of studies are being conducted to this date, most of the studies are not likely to be available on the real sites. Therefore, this study provides construction managers with a methodology of drawing construction performance indicators based on productivity analysis using Artificial Neural Network (ANN) models and Web-CYCLONE. This methodology is expected to have high utilization and precision of construction operation planning and management.