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안기언(Ahn Ki-Uhn),박철수(Park Cheol-Soo) 한국건축친환경설비학회 2011 한국건축친환경설비학회 학술발표대회 논문집 Vol.2011 No.3
The purpose of this study is to investigate the impact of uncertain parameters on PMV simulation runs. The uncertain parameters were chosen from the literature and the uncertainty propagation is introduced to treat the parameters which are not deterministic but stochastic. The Latin Hypercube Sampling (LHS) method, one of the Monte Carlo techniques, was selected for uncertainty propagation and die EnergyPlus was selected as an analysis simulation tool for the thermal comfort analysis. All of the precesses was automated in the Matlab platform. In the paper, tile impact of uncertainty on thermal comfort assessment in simulation are addressed.
시간 분할 기법을 이용한 냉난방 에너지 추정 및 벤치마킹
안기언(Ki Uhn Ahn),김덕우(Deuk-Woo Kim),이승언(Seung-Eon Lee),채창우(Chang-U Chae),조현미(Hyun Mi Cho) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
Existing building energy benchmarking is evaluated based on the total energy use intensity, making it difficult to evaluate the relative cooling and heating energy determined by seasonal building characteristics and operation. An Information Gain based Temporal Segmentation(IGTS) method, an unsupervised segmentation technique), was used to identify the seasonal transition times in patterns of hourly weather and building energy use. This study classified four seasons by IGTS for 12 commercial buildings and estimated the base-load, cooling and heating energy. For 12 buildings, the estimated and measured heating and cooling energy during the summer and winter periods showed a linear relationship of R² 0.966, and those of rank difference in benchmarking results is marginal.
안기언(Ki Uhn Ahn),김경재(Kyung-Jae Kim),김덕우(Deuk-Woo Kim),채창우(Chang-U Chae),조현미(Hyun Mi Cho) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
Physics models and machine learning (ML) models are used to describe the behaviour of variable refrigerant flow (VRF) systems. This study introduces a hybrid model that combines physics and ML models to achieve enhanced usability and reliability compared to traditional models. A Bayesian neural network (BNN) was used to develop traditional ML and hybrid models for predicting the compressor power of a VRF system, and the predictive abilities and uncertainties of both the models were investigated. For the experimental dataset, the predictive performances of both the models were similar; however, the epistemic uncertainty of the hybrid model quantified using the BNN was 36.4% lower than that of the ML model.
안기언(Ki Uhn Ahn),김경재(Kyung-Jae Kim),김덕우(Deuk-Woo Kim),채창우(Chang-U Chae),조현미(Hyun Mi Cho) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
Physics models and machine learning (ML) models are used to describe the behaviour of variable refrigerant flow (VRF) systems. This study introduces a hybrid model that combines physics and ML models to achieve enhanced usability and reliability compared to traditional models. A Bayesian neural network (BNN) was used to develop traditional ML and hybrid models for predicting the compressor power of a VRF system, and the predictive abilities and uncertainties of both the models were investigated. For the experimental dataset, the predictive performances of both the models were similar; however, the epistemic uncertainty of the hybrid model quantified using the BNN was 36.4% lower than that of the ML model.
안기언(Ki Uhn Ahn),김경재(Kyung-Jae Kim),김덕우(Deuk-Woo Kim),채창우(Chang-U Chae),조현미(Hyun Mi Cho) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
Physics models and machine learning (ML) models are used to describe the behaviour of variable refrigerant flow (VRF) systems. This study introduces a hybrid model that combines physics and ML models to achieve enhanced usability and reliability compared to traditional models. A Bayesian neural network (BNN) was used to develop traditional ML and hybrid models for predicting the compressor power of a VRF system, and the predictive abilities and uncertainties of both the models were investigated. For the experimental dataset, the predictive performances of both the models were similar; however, the epistemic uncertainty of the hybrid model quantified using the BNN was 36.4% lower than that of the ML model.
안기언(Ahn, Ki-Uhn),김덕우(Kim, Deuk-Woo),김영진(Kim, Young-Jin),윤성환(Yoon, Sung-Hwan),박철수(Park, Cheol-Soo) 한국건축친환경설비학회 2012 한국건축친환경설비학회 학술발표대회 논문집 Vol.2012 No.10
Dynamic modeling of an existing building as operated requires much more efforts, insights and understanding than dynamic modeling of that as designed. This paper will report, how the authors (modelers) developed a dynamic energy simulation model for a large office building (33 stories above ground and 6 underground levels, a total floor area of 91,898.28 m2). The simulation project, inclusive of modeling, calibration and validation, was finished within a tight time schedule (2 months) by four people (1 M.S. and 3 Ph.D. students). It is widely known that even the most advanced simulation tool can’t simulate as operated the complex physical heat and mass transport phenomena in a building, and complex relationship between mechanical systems and components. In the paper, the following issues will be elaborated: (1) gathering building information (architectural, mechanical, electrical, schedules, hourly climate, etc.), (2) handling disagreement between construction documents and a reality, (3) dealing with absent (unknown) and uncertain inputs, and (4) tweaking the simulation model when the tool can’t simulate as it is. Finally, the paper will show the comparison between the simulation prediction and measured energy use.
안기언(Ahn Ki-Uhn),김영진(Kim Young-Jin),박철수(Park Cheol-Soo),김인한(Kim In-Han) 대한건축학회 2012 대한건축학회논문집 Vol.28 No.5
BIM(Building Information Model) enables information sharing and reuse for interoperability between prevalent software tools in the AEC(Architecture, Engineering, and Construction) industry. Although a BIM based energy simulation tool can reduce costs and time required for the simulation work, no practical interface between CAD softwares and dynamic energy analysis tools has been developed so far. With this in mind, this paper suggests two approaches (Full Automated Interface and Semi Automated Interface) enabling information transition from CAD tools (e.g., IFC) to EnergyPlus input file, IDF. FAI, if ideally developed, can convert IFC to IDF based on the use of pre-defined defaults without requiring human intervention. In contrast, SAI converts geometry information only out of IFC to IDF and then require human data entry for uncertain simulation inputs. For this study, a library building was chosen and space boundary generated from ArchiCAD 13 was employed for geometry mapping. The Morris method, one of sensitivity analysis methods, was used for identifying significant inputs. In FAI and SAI, dominant inputs, out of the Morris method, expresses were used for Monte Carlo simulation to quantify probablistic simulation outputs. In the paper, FAI and SAI simulation results are cross-compared, and pros and cons of FAI and SAI are discussed.
시간 분할 기법을 이용한 냉난방 에너지 추정 및 벤치마킹
안기언(Ki Uhn Ahn),김덕우(Deuk-Woo Kim),이승언(Seung-Eon Lee),채창우(Chang-U Chae),조현미(Hyun Mi Cho) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
Existing building energy benchmarking is evaluated based on the total energy use intensity, making it difficult to evaluate the relative cooling and heating energy determined by seasonal building characteristics and operation. An Information Gain based Temporal Segmentation(IGTS) method, an unsupervised segmentation technique), was used to identify the seasonal transition times in patterns of hourly weather and building energy use. This study classified four seasons by IGTS for 12 commercial buildings and estimated the base-load, cooling and heating energy. For 12 buildings, the estimated and measured heating and cooling energy during the summer and winter periods showed a linear relationship of R² 0.966, and those of rank difference in benchmarking results is marginal.
시간 분할 기법을 이용한 냉난방 에너지 추정 및 벤치마킹
안기언(Ki Uhn Ahn),김덕우(Deuk-Woo Kim),이승언(Seung-Eon Lee),채창우(Chang-U Chae),조현미(Hyun Mi Cho) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
Existing building energy benchmarking is evaluated based on the total energy use intensity, making it difficult to evaluate the relative cooling and heating energy determined by seasonal building characteristics and operation. An Information Gain based Temporal Segmentation(IGTS) method, an unsupervised segmentation technique), was used to identify the seasonal transition times in patterns of hourly weather and building energy use. This study classified four seasons by IGTS for 12 commercial buildings and estimated the base-load, cooling and heating energy. For 12 buildings, the estimated and measured heating and cooling energy during the summer and winter periods showed a linear relationship of R² 0.966, and those of rank difference in benchmarking results is marginal.