Seungho Shin

Dohun Kwon

Frans Michael

Fatemeh Eftekhar

Suyeon Bang

Jaehyun Go

Hyeong Seok Lee

Jiwook Byun

Chulho Kim

The Urbane2 Lab is seeking Ph.D. and Integrated M.S.-Ph.D. students. Candidates should demonstrate strong initiative and ambition in research and development.

Urbane2 Lab에서는 연구 개발에 대한 강한 의지와 야망을 가진 박사 및 석박사 통합 과정 학생을 모집한다.

Exploration of key design questions in rule-based shading control for building energy load reduction

Weather-adaptive rule-based mixed-mode ventilation in a tropical climate: simulation and real-world validation

A new plenum-based dual-mode passive cooling system enabling naturally ventilated core zones in office buildings

Toward a new paradigm for urban climate modelling: challenges and opportunities

A new passive heating design on the basis of phase change materials tailored to residential buildings

Interpreting complex relationships between urban and meteorological factors and street-level urban heat islands: Application of random forest and SHAP method

Optimal partial-load operation strategies of surface water-source centrifugal heat pumps with thermal energy storage for large buildings

A novel control strategy for optimizing combined cooling, heating, and power systems with energy storage devices in commercial buildings

Integrating Doppler LiDAR and machine learning into land-use regression model for assessing contribution of vertical atmospheric processes to urban PM2.5 pollution

Development and evaluation of a buoyancy-driven airflow window with multioperation modes

Multi-criteria evaluation of poly-generation system applied to commercial buildings with various prime movers and building types

A living lab to develop smart home services for the residential welfare of older adults

Evaluation of multi-layer urban canopy model (MLUCM) for urban microclimate predictions at different urban contexts

Structured stochastic models based on multi-source heterogeneous data for predicting internal electricity load of non-residential buildings

Dynamic rule-based change-over ventilation strategy with weather-responsive air-conditioning setpoints

Quantifying Photovoltaic surplus at an urban scale. A case study in Seoul

Exploring the impact of urban factors on land surface temperature and outdoor air temperature. A case study in Seoul, Korea

A plan to build a net zero energy building in hydrogen and electricity-based energy scenario in South Korea

An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment

Assessing the spatiotemporal characteristics, factor importance, and health impacts of air pollution in Seoul by integrating machine learning into land-use regression modeling at high spatiotemporal resolutions

Reliability, economic, and environmental analysis of fuel-cell-based hybrid renewable energy networks for residential communities

Battery-H2 storage system for self-sufficiency in residential buildings under different electric heating system scenarios

Structured probabilistic models for capturing household and temporal variations in the internal electricity load

Weather-based operation strategy for a dynamically compartmentalized double-skin facade system

Simplified data-driven models for model predictive control of residential buildings

Dynamic compartmentalization of double-skin facade for an office building with single-sided ventilation

Multi-objective optimization of the multi-story residential building with passive design strategy in South Korea

Evaluation of model fidelity for solar analysis in the context of distributed PV integration at urban scale

The effect of spatial heterogeneity in urban morphology on surface urban heat islands

Scrutinizing modeling and analysis methods for evaluating overheating risks in passive houses

Effective and scalable modelling of existing non-domestic buildings with radiator system under uncertainty

Investigating an adequate level of modelling for retrofit decision-making, A case study of a British semi-detached house

Simplified vector-based model tailored for urban-scale prediction of solar irradiance

A data-centric bottom-up model for generation of stochastic internal load profiles based on space-use type

Predicting intra-day profiles under time-of-use tariffs using smart meter data

Influence of error terms in Bayesian calibration of energy system models

A review of uncertainty analysis methods in building energy assessment

Bayesian inference of structural error in inverse models of thermal response tests

Exploration of the Bayesian Network framework for modelling window control behaviour

Exergy analysis of a hybrid ground-source heat pump system

Sensitivity analysis methods for building energy models, comparing computational costs and extractable information

Exploring the impact of different parameterisations of occupant-related internal loads in building energy simulation

Efficient energy modelling of heterogeneous building portfolios

Relative importance of factors influencing building energy in urban environment

Scalable methodology for large scale building energy improvement, relevance of calibration in model-based retrofit analysis

Evaluation of calibration efficacy under different levels of uncertainty

Measurement and verification of building systems under uncertain data, a Gaussian process modeling approach

Uncertainty quantification of microclimate variables in building energy simulation

MPC-based appliance scheduling for residential building energy management controller

Quantitative risk management for energy retrofit projects

Gaussian process modeling for measurement and verification of building energy savings

Calibration of building energy models for retrofit analysis under uncertainty

A predictive model for computing the influence of space layouts on nurses’ movement in hospital units

Unit-related factors that affect nursing time with patients, applying spatial analysis to the nurses’ time and motion study

Reducing the gap between predicted and measured urban microclimate through handling parameter uncertainty and model form uncertainty

Techno-Economic Analysis of Hybrid Battery-Hydrogen Storage System for Passive and Conventional Residential Buildings

Design and analysis of a window-integrated passive system (WIPS) with the use of solar heat gains

Energy performance of a hybrid DSF-inspired solar heating façade for office buildings

Design and analysis of a renewable energy network based on fuel cells in a residential community

Effect of limited controller and sensor datasets on the performance of data-driven model predictive control for residential buildings

The design of distributed control system for a hybrid energy supply network in a residential community

Spatio-temporal data analysis for development of microclimate prediction models

The effect of urban spatial characteristics on microclimate

Towards a comprehensive model of window control behaviour, a survey-based investigation on interdisciplinary drivers in Danish dwellings

Evaluation of temperature dependent models for PV yield prediction

Introducing thermal comfort attitudes, psychological, social and contextual drivers in occupant behaviour modelling with Bayesian Networks

A simplified vector-based method for irradiance prediction at urban scale

Efficiency and reliability of Bayesian calibration of energy supply system models

A functional principal components model for internal loads in building energy simulation

Exergy analysis of a ground-source heat pump system

Monitoring and stimulating energy behavioural change in university buildings towards post carbon cities

Investigating an adequate level of modelling for energy analysis of domestic buildings

New extension of Morris method for sensitivity analysis of building energy models

Data driven bottom-up approach for modelling internal loads in building energy simulation using functional principal components

Data driven model for rooftop excess electricity generation

Parameterisation of internal loads in assessment of building energy performance

High resolution energy simulations at city scale

Variable importance analysis for urban building energy assessment in the presence of correlated factors

Cost-effective measurement and verification method for determining energy-savings under uncertainty

Stochastic energy simulation for risk analysis of energy retrofits

Scalable methodology for energy efficiency retrofit decision analysis

Testing a streamlined project evaluation tool for risk-conscious decision making, the Chicago Loop energy efficiency retrofit initiative

Risk analysis of energy-efficiency projects based on Bayesian calibration of building energy models

Analysis methodology for large organizations investment in energy retrofit of buildings

HVAC design informed by organizational simulation

Harmonizing the client’s processes with space planning decisions

A modeling approach for estimating the impact of spatial configuration on nurses’ movement

Lessons from an advanced building simulation course

A study of variations among Mies’ courtyard houses by a combined set of visual and environmental properties

건축 부재 일체형 상변화물질 기반 패시브 난방 시스템 설계 시스템 및 방법

신재생 에너지의 동적 전력 관리 시스템

구조화된 확률적 통계모델을 활용한 비주거 건축물의 전력 에너지 수요예측 방법

전환 환기 전략을 이용하는 냉난방 시스템

건물 차양장치 제어 시스템

부력을 이용한 패시브 환기 시스템 및 이를 이용한 환기방법

동적운영에 의해 외기 및 내기의 유동제어가 가능한 냉난방 에너지 절감 시스템

냉난방 에너지 절감을 위한 건물의 패시브 이중외피 동적구획화 시스템

확률적 통계모델을 이용한 주거 커뮤니티 전력 및 급탕 부하 수요 예측 방법

A review of uncertainty analysis in building energy assessment

Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information

Unit-related factors that affect nursing time with patients: Spatial analysis of the time and motion study

Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty

Uncertainty quantification of microclimate variables in building energy models

Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach

Evaluation of Calibration Efficacy under Different Levels of Uncertainty

A predictive model for computing the influence of space layouts on nurses' movement in hospital units

Ruchi Choudhary

Godfried Augenbroe

Kathrin Menberg

Ralph T Muehleisen

Tageui Hong

Wei Tian

Sonit Bafna

Da Yan

Victor M Zavala

Jihyeon Cho

Pieter de Wilde

Xiaohang FENG

Steve H.L. Yim

Nari Yoon

Ryozo Ooka

Wonjun Choi

Rebecca Mary Ward

Chen Chen

Jianhui Wang, Professor, IEEE Fellow

Yeonsook Heo

Retrofitting existing buildings is urgent given the increasing need to improve the energy efficiency of the existing building stock. This paper presents a scalable, probabilistic methodology that can support large scale investments in energy retrofit of buildings while accounting for uncertainty. The methodology is based on Bayesian calibration of normative energy models. Based on CEN-ISO standards, normative energy models are light-weight, quasi-steady state formulations of heat balance equations, which makes them appropriate for modeling large sets of buildings efficiently. Calibration of these models enables improved representation of the actual buildings and quantification of uncertainties associated with model parameters. In addition, the calibrated models can incorporate additional uncertainties coming from retrofit interventions to generate probabilistic predictions of retrofit performance. Probabilistic outputs …

Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy …

This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time …

Though sensitivity analysis has been widely applied in the context of building energy models (BEMs), there are few studies that investigate the performance of different sensitivity analysis methods in relation to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models. We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morris method for parameter screening, (b) linear regression analysis (medium computational costs) and (c) Sobol method (high computational costs). It is revealed that the results from Morris method taking the commonly used measure for parameter influence can be unstable, while using the median value yields robust results for evaluations with small sample sizes. For the dominant parameters the results from all three sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameter …

We present a Gaussian process (GP) modeling framework to determine energy savings and uncertainty levels in measurement and verification (M&V) practices. Existing M&V guidelines provide savings calculation procedures based on linear regression techniques that are limited in their predictive and uncertainty estimation capabilities. We demonstrate that, unlike linear regression, GP models can capture complex nonlinear and multivariable interactions as well as multiresolution trends of energy behavior. In addition, because GP models are developed under a Bayesian setting, they can capture different sources of uncertainty in a more systematic way. We demonstrate that these capabilities can ultimately lead to significantly less expensive M&V practices. We illustrate the developments using simulated and real data settings.

Objective The primary goal of this study was to test the hypothesis that nurses adopt distinct movement strategies based on features of unit topology and nurse assignments. The secondary goal was to identify aspects of unit layout or organization that influence the amount of time nurses spend in the patient room. Background Previous research has demonstrated a link between nursing hours and patient outcomes. Unit layout may affect direct patient care time by determining aspects of nurse behavior, such as the amount of time nurses spend walking. The recent nurses' Time and Motion study employed multiple technologies to track the movements and activities of 767 medical-surgical nurses. With regard to unit layout, initial analysis of the data set did not detect differences between types of units and time spent in the patient room. The analysis reported here applies novel techniques to this data set to examine the …

The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO 2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustrates both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty.

Retrofitting of existing buildings is essential to reach reduction targets in energy consumption and greenhouse gas emission. In the current practice of a retrofit decision process, professionals perform energy audits, and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions. In order to enhance the reliability of simulation models, they typically calibrate simulation models based on monitored energy use data. The calibration techniques used for this purpose are manual and expert-driven. The current practice has major drawbacks:(1) the modeling and calibration methods do not scale to large portfolio of buildings due to their high costs and heavy reliance on expertise, and (2) the resulting deterministic models do not provide insight into underperforming risks associated with each retrofit intervention.

The effects of urban morphology on urban climates have been investigated mainly in terms of average urban spatial characteristics represented by the mean values of urban spatial variables, such as average building height of an urban area of interest. This study examines the potential impacts of spatial heterogeneity on surface urban heat island (SUHIs) in comparison with those of average spatial values through a case study of the Greater London and Seoul areas. The standard deviation measure of spatial variables was used to indicate spatial heterogeneity, specifically spatial variation within an urban area. Methods were developed to extract a comprehensive set of spatial characteristics from the GIS data. Land surface temperatures (LSTs) of four different time periods were used to analyze the SUHIs distribution across the two cities. Correlation analyses were conducted to investigate correlations among spatial …

The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn one's attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to …

In this study, the multi-objective optimization of passive design strategy was conducted for multi-story residential buildings in South Korea. The performance of passive design was analyzed in terms of energy, environmental, and economic metrics, considering the life cycle performance. Various passive design factors were considered to better reflect the actual phenomenon, and a novel simulation modeling method which includes building model simplification and meta model was developed for an accurate and fast calculation of optimization. The sensitivity analysis was conducted to decide critical design factors in optimization and showed that the most influential design factors are airtightness, occupants, and window-to-wall ratio. Throughout optimization, the energy, environmental impact, and economic feasibility in multi-story residential building can be improved by 52.7%, 39.5%, and 36.9%, respectively. The …

Extended literature reviews confirm that the accurate evaluation of energy-related occupant behaviour is a key factor for bridging the gap between predicted and actual energy performance of buildings. One of the key energy-related human behaviours is window control behaviour that has been modelled by different probabilistic modelling approaches. In recent years, Bayesian Networks (BNs) have become a popular representation based on graphical models for modelling stochastic processes with consideration of uncertainty in various fields, from computational biology to complex engineering problems. This study investigates the potential applicability of BNs to capture underlying complicated relationships between various influencing factors and energy-related behavioural actions of occupants in buildings: in particular, window opening/closing behaviour of occupants in residential buildings is investigated. This …

Uncertainty in sensor data (e.g., weather, occupancy) complicates the construction of baseline models for measurement and verification (M&V). We present a Monte Carlo expectation maximization (MCEM) framework for constructing baseline Gaussian process (GP) models under uncertain input data. We demonstrate that the GP-MCEM framework yields more robust predictions and confidence levels compared with standard GP training approaches that neglect uncertainty. We argue that the approach can also reduce data needs because it implicitly expands the data range used for training and can thus be used as a mechanism to reduce data collection and sensor installation costs in M&V processes. We analyze the numerical behavior of the framework and conclude that robust predictions can be obtained with relatively few samples.

In contrast to energy analysis, the analysis of exergy allows the evaluation of the quality of different energy flows and enables a comprehensive assessment of inefficiencies within a system and its individual components by accounting for exergy consumption. While exergy analysis methods have been applied to a variety of conventional and renewable energy supply systems, there is still a lack of knowledge regarding the exergy flows and exergy efficiency of hybrid ground-source heat pump systems with a supplementary boiler. In this study, we develop a thermodynamic model for each subsystem in a hybrid heating and cooling system of an existing building by applying the concept of cool and warm exergy. A comparison of the exergy consumption of the hybrid system in heating and cooling reveals that there are significant differences regarding the components that attribute most to the overall exergy consumption …

This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.

Spatial design influences organizational productivity of healthcare by affecting how healthcare providers use the space. Current healthcare models are not very relevant for space layout and planning because they represent spatial variables through very simplistic measures such as path distances and number of physical resources. Adding syntactic variables of space to these models would derive the effects of alternative layouts better and in more realistic ways. This article presents a generalized linear model that predicts the effect of different layouts of hospital units on nurses' movement patterns. Results from the model show that spatial properties of a nurse's assignment have a predictable impact on the frequency of trips that he or she makes to the patient rooms during a shift. The model is shown to have explanatory power and can be used to predict the movement patterns of nurses for units that are roughly …

This article presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This article demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the energy service company industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach with standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.

Optimization of nurses’ movement is an important means for improving organizational productivity in healthcare units. Studies on movement and behavior of nurses have often found that the spatial layout of nursing unit floors has a significant effect on nurses’ mobility. However, efforts to correlate types of hospital layouts with nurses’ movement have not met with consistent success. We show that the effect of spatial layout on nurses’ movement can be modeled with far greater predictability and consistency if the unit of correlational analysis is not the gross scale of the entire unit floor, but at the finer scale of an “assignment:” ie the specific set of rooms to which individual nurses are assigned during a shift. Choosing a nurses’ assignment as a unit of analysis follows our main argument that nurses will modify or adapt their behavior according to the characteristics of the area within which they work, rather than to an entire floor plate typology. This paper presents a study that demonstrates this argument empirically. The study is based on an analysis of data collected as part of a Time and Motion study organized by Ascension Health and Kaiser Permanente, from medicalsurgical nursing units located in 36 clinically diverse hospitals. Movement patterns were captured using RFID tags worn by nurses, producing time-sequence spatial data that provide a nurse’s location and time when the nurse comes in the location and went out of the location with a time stamp. From this data, we used 143 individual nurses’ shifts in five of these units to model the frequency of visits made by nurses to their assigned patient rooms. We found that a generalized linear model …

Urban and meteorological factors strongly affect street-level urban heat islands (UHIs), but few studies have considered their interactions under varying weather conditions. This study investigated the relationship between urban and meteorological factors and street-level UHI intensity in Seoul during daytime and nighttime in summer. UHI intensity was calculated from urban air temperatures measured by 568 street-level sensors. Random forest regression models and Shapley Additive exPlanations (SHAP) method were used to quantitatively analyze nonlinear relationships and interaction effects of the predictors. The results indicated that meteorological variables, particularly regional air temperature, significantly influenced UHI intensity during both daytime and nighttime. Furthermore, urban factors such as building coverage ratio and pervious ratio became more important during nighttime. Both meteorological and …

This paper presents a simplified vector-based model on the basis of consideration of the urban context to effectively predict the solar potential of urban areas. The proposed model is based on vector-based methods without the use of ray trace and ray interception techniques, yet consists of new methods that suitably account for the nonuniform solar radiation of the sky, obstruction by urban surfaces, and reflection by urban surfaces in urban areas. The proposed model establishes three new methods to simplify the calculation in the context of urban applications: (1) a two-segment discretization model, (2) an edge angle detection obstruction model, and (3) a unified view-angle-based reflection model. The performance of the method was compared against the advanced daylight simulation program RADIANCE and measurements obtained from controlled experiments. The first comparison demonstrated the new …

Professor, School of Civil, Architectural, and Environmental Engineering

Associate Professor, School of Civil, Architectural, and Environmental Engineering

UE2 Lab

교수님 소개

허연숙

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Exploration of key design questions in rule-based shading control for building energy load reduction

Weather-adaptive rule-based mixed-mode ventilation in a tropical climate: simulation and real-world validation

A new plenum-based dual-mode passive cooling system enabling naturally ventilated core zones in office buildings

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

허연숙

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Exploration of key design questions in rule-based shading control for building energy load reduction

Weather-adaptive rule-based mixed-mode ventilation in a tropical climate: simulation and real-world validation

A new plenum-based dual-mode passive cooling system enabling naturally ventilated core zones in office buildings

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집