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Purpose: Building design optimization could be performed in various ways. In most cases, potential design alternatives are evaluated against multi-criteria performance indicators through iterative create-test processes. Especially for the early design stage, optimum passive design of a building tends to be more critical and needs to be investigated rigorously. This study focuses on building passive design optimization in different global regions considering their climatic characteristics. Site location, building orientation, wall construction, and glazing assemblies are considered to be primary passive design variables. Objective functions for optimization include occupancy comfort, capital cost, and life-cycle cost. Method: By utilizing Design Builder’s genetic-algorithm-based optimization tool, a single-room type highly simplified spatial unit virtually being located in 5 different global regions (Jakarta, Cairo, Buenos Aires, Seoul, and Resolute) is evaluated and optimized against two essential design criteria. Result: It is turned out that passive building design in different climatic contexts may lead to a different set of strategies to minimize occupant’s discomfort and life-cycle cost. The research outcome shows that the lowest solar transmission glazing assembly, which is critical for optimum passive design strategy in cooling-dominated regions may not function properly in the locations where tropical hot-humid weather prevail.

Even in case ventilation system is operating in optimal fashion, indoor air quality could be varied depending on the indoor contaminant release occasions. Thus, accurate and rapid identification of...

Air distribution system could critically affect SARS-CoV-2 transmission in indoor space; therefore, this study aims at demonstrating numerical characteristics of SARS-CoV-2 migration with varied air distribution system configurations. Seven cases were investigated regarding three major aspects: how fast suspended particles can be removed from the ventilated space or changed into deposited particles; how much particles are attached to various object surfaces which leads to an infection by touching fomite. All cases were analyzed through computational fluid dynamics (CFD). Both different shapes (round or linear diffusers) and installation locations (ceiling or floor) of inlet and outlet diffusers were investigated. Results showed that different air distribution system would lead to different dispersion profiles of infectious particles and different deposition pattern of particles on interior surfaces. With the same air flow rate, linear-diffuser would perform better for CO2 extraction while requiring less time to remove or collide the same magnitude of suspended droplets compared to round-diffuser. However, how quickly removed or suspended droplets collide is not proportional to how less the number of total particles are remained. Two additional cases with double sized space possessing best ventilation configuration were also examined to explore potential application of the best-ventilated configuration to various spatial expansion cases.

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations, however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

Semi-transparent photovoltaic (STPV) windows, one of the building façade elements, can generate electricity and provide a certain amount of daylight for occupants. Nevertheless, expensive cost and unsatisfying indoor daylight performance in the room are common problems with STPV windows. This study investigates the thermal, daylight, energy, and life-cycle cost performance of STPV windows by considering varied window-to-wall ratios, building orientations, and STPV module types. The electricity balance index (elBI) indicator is proposed as one of the performance evaluation criteria. Two types of building models are established for this study: a rig-test building as the baseline building model and a KAIST campus research facility as the test building model along with the actual measurements and simulations using DesignBuilder. Results show that the STPV window in the Mediterranean climate demonstrates higher efficiency based on the elBI indicator. Decision-making analysis using the analytic hierarchy process and PROMETHEE II found weighting rates of 0.309, 0.076, and 0.465 for elBI, comfort, and cost criteria, respectively. Furthermore, lighting energy consumption becomes a critical variable for STPV module type selection, while a simple ON/OFF lighting control system can improve the elBI value by 0.02 ~ 0.04. Our research findings could potentially improve the decision-making process for building and urban energy systems selection in different climate types.

Air distribution systems generally focus on diluting contaminants and distributing conditioned air. However, this strategy has a potential for spreading particulate matter or bioaerosols. Thus, it ...

The energy consumption behaviour in campus buildings are less well understood than other non-domestic buildings. Energy simulation analysis is a beneficial method in measuring building energy consumption as well as investigating different energy management strategies for the existing buildings. This research has been established through investigating the heating and cooling load reduction potential of green wall as an energy saving measure for three different activity buildings (i.e., research, dormitory and administration) at KAIST campus, using a simulation tool. The focus has been facilitated while exploring how different functional buildings act in response to vertical vegetation in terms of energy savings, considering operational pattern, occupancy schedule and physical characteristics as the deciding parameters. Maximum occupancy during night time in dormitory turned out to be advantageous for reduced heating energy consumption (60%) compared to the research (7%) and administrative (3%) building, which are mostly cooling and day-time dominated. This is because; the largest savings in heating due to vegetation were associated with extreme temperature condition, which is close to zero or sub-zero. Conversely, in summer, vegetation led to become less effective in saving cooling energy, especially for the research and administrative facility where cooling is crucial due to daytime dominated occupancy. Moreover, the cooling energy savings was found to be closely associated with the area covered by plants irrespective of opaque wall and fenestrations

The aim of this research is to investigate the effectiveness of vertical vegetation in terms of energy savings for a residential facility situated at KAIST campus. The research has been established through analyzing different building orientations to find out the most suitable combination of vegetation and orientation for reduced heating and cooling energy consumption. A simulation model has been developed where leaf area index, one of the contributing plant physiological parameters for improving building thermal performance, has been incorporated as per seasonal variations. This allowed observing thermal performance patterns of green wall throughout the year. Approximately 60 % savings in heating energy and 31 % increase in overall energy efficiency were achieved with the non-insulated studied building case, and the results showed extreme weather conditions lead to greater energy savings in winter. In cooling season, plant layers were found to be less effective in terms of facade thermal performance especially during relatively higher temperature period, with an average of 17 % cooling energy savings. The North-oriented green wall was observed to be the most effective in increasing heating energy efficiency, while the East-oriented wall was observed to be greatest in cooling energy savings. A higher LAI value proved to be beneficial in improving both heating and cooling energy performance for the studied building.

Despite the advantages of building enclosure systems for improving ventilation efficiency and reducing energy consumption, there is a large difference in performance from the central heating, ventilating, and air-conditioning (HVAC) system. This study investigated the performance of a smart window-integrated ventilation (SWV) system in conjunction with a centralized HVAC system for enhancing indoor air quality (IAQ) and thermal comfort in a typical single-occupancy office. To evaluate the performance of two coupled systems, IAQ and thermal comfort in an office were analyzed for satisfying seasonally optimal ventilation scenarios using computational fluid dynamics simulations. A set of scenarios for case study was determined by outdoor temperature, supply airflow rate and temperature, and operation strategy of the two systems. Numerical results showed that using the SWV system as an aid to the HVAC system was more effective in winter than in summer for improving IAQ and thermal comfort. Only in the summer, the SWV led to poor IAQ. In spring and autumn, there were scenarios in which the SWV system performed similar to the HVAC system. Therefore, depending on indoor and outdoor conditions, the SWV system can be a good choice as an auxiliary means of the HVAC system or for single operation.

As a special form of microgrid, an apartment to grid (A2G) system is conceptually designed and proposed for demand and cost sensitive energy management based on the energy implications and electricity consumption characteristics of apartment complexes in Korea. The A2G system aggregates generators as well as loads, and its energy management decisions are made by A2G System Manager with energy profiles from the aggregated components in the system. Fundamental factors such as energy policy, energy optimization criteria, and fair benefit sharing issues are dealt with exemplary scenarios and case studies. Load shifting/shaping can be easily achieved by the coordination of the A2G system components which are already closely and densely aggregated unlike those components in a typical microgrid as well as V2G. Then, a network of A2Gs, namely an A2G Network is introduced as a business model to promote the benefit of aggregation. The main contribution of the proposed A2G system comes from it's innate nature, aggregation of its components; both economic benefits as well as system stability could potentially be achieved through more effective load shifting/shaping by the coordination of the existing aggregated A2G system components.

In recent years, the notion of intelligent buildings (IBs) has become increasingly popular due to their potentials for deploying design initiatives and emerging technologies towards maximized occupants’ comfort and well-being with sustainable design. However, various definitions, interpretations, and implications regarding the essence of IBs exist. Various key performance indicators of IBs have been proposed in different contexts. This study explores the notion of IBs and presents an analysis of their main constituents. Through a comparison of these constituents in different contexts, this study aims to extract the common features of IBs leading to an evolved definition which could be useful as a reference framework for design, evaluation, and development of future IBs. Findings also scrutinize the long run benefits of IBs, while demonstrating the constraints and challenges of the current international interpretations.

This study evaluated indoor environmental characteristics in an office room equipped both with ventilation win-dow system and central cooling system. Fresh air is supplied only by the central cooling system whereas indoor air is discharged outside through both ceiling diffuser and a ventilation window system. Numerical study is conducted by changing the volumetric flow rates of exhaust ports of each system. For estimating the performance of this cou-pled system, CO 2 concentration and Predicted Mean Vote(PMV) were calculated using Computational Fluid Dynamics (CFD) simulation. The more the ceiling diffuser exhausts indoor air, the more the CO 2 concentration decreases. However, when the ventilation window system exhausts more indoor air, thermal comfort level gets improved in the office room with cooling system. Therefore, when the ventilation window system is operated, the coupled operation with central cooling system should be considered for enhancing indoor air quality and thermal comfort, together.

In architectural planning, previous cases of similar spatial program provide important data for architectural design. Case-based reasoning (CBR) paradigm in the field of architectural design is closely related to the designing behavior of a planner who makes use of similar architectural designs and spatial programs in the past. In CBR, spatial graph can be constituted with most fundamental data, which can provide a method of searching spatial program by using visual graphs. This study developed a system for CBR that can analyze the similarity through graph comparison and search for buildings. This is an integrated system that is able to compare space similarity of different buildings and analyze their types, in addition to the analysis on a space within a single structure.

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

In this paper, we propose a movement classification model using acceleration spectrogram combined with dynamic time warping method. First, the proposed system collects data sets from a mobile device embedded acceleration sensor, and filters only coordinate data, which has an effect on the states of movements. Then the data sets in the form of frequency are converted into spectrograms. The RGB color vectors in these spectrograms are used to classify the states of movements. In order to classify these changes, a DTW algorithm is used. Finally, we validate the feasibility and applicability of the proposed model by implementing and applying it to three typical human navigational movements, namely, plain walking, going upstairs and going downstairs.

This study aims at providing an economic assessment for incineration plants which recover heat during its incineration pro-cess. In this study, Life Cycle Cost(LCC) of incineration plants is performed based on each regression analysis formula for con-struction cost, operation cost, and heat generation in order to compare economic feasibility. The result shows that the incinerationplant recovering waste heat while processing 80 tons of waste per day increases both initial investment and operation cost butthis type of an incineration plant has economical predominance from the recovered waste heat over the one that does not recoverheat when being operated for more than eleven years if the retrieved heat replaces the use of LNG. And its payback time reachesmore than 19 years in case of selling heat and performing emission trading.Key words : Incineration plant, Waste heat recovery, Life cycle cost, Regression analysis, Economic assessment · Received : December 12, 2013 · Revised : February 11, 2014 · Accepted : April 2, 2014Corresponding Author : Seongju Chang (E-mail : schang@kaist.ac.kr)Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, W16-404, 291Daehak-ro, Yuseong-gu, Daejeon 305-701, KoreaTel : +82-42-350-3627 / Fax : +82-42-350-4540ⓒThe Korean Institute of Resources Recycling. All rights reserved. This is an open-access article distributed under the termsof the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permitsunrestricted non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.

Building energy consumption takes up almost 25% of the total energy consumption. Therefore, diversified ways, such as improving wall and window insulation, have been considered to reduce building energy consumption. Recently, green roof system has been explored as an effective alternative for dealing with reducing heating and cooling energy, thermal island effect and improving water quality. However, recent studies regarding a green roof system have only focused on building energy reduction without considering the applied usage, location, and story of the green roof system. Therefore, this study pays attention to the heating and cooling energy in relation to the applied usage, location, and story of a green roof system for investigating its impact on energy reduction. The result of simulations show that the reduction in heating energy consumption is higher when applied to Cherwon-gun province which has a continental climate condition, compared to the city of Busan that is distinguished by its warm climate. Cooling energy saving turns out to be higher when the green roof system is applied to Busan in comparison with Cherwon. As for the applied usage or function of the building, residential space acquires the highest heating and cooling energy saving effect rather than commerce, educational or office space because of HVAC"s running time based on usage. When it comes to the story of the green roof, both heating and cooling energy saving become the highest when the green roof is applied to single-storied buildings. The reason is that single story building is affected by the ground largely. Generally, the variations of heating energy consumption are larger than the cooling energy consumption. The outcome of the simulations, when a green roof system is applied, indicates that the energy consumption reduction rate is dynamically responding to the applied usage, location, and story. Therefore, these factors should be counted closely for maximizing the reduction of energy consumption through green roof systems.

Recently, the coverage of urban forests has been rapidly decreasing as the cities are created and expanding. Consequently, there arise urban problems such as heat island effect, urban flooding, urban desertification and so on. In this context, green roof systems is considered to be an efficient alternative to deal with these problems. However, it is difficult to apply green roof to new buildings since the majority of the buildings in cities are already constructed and the demand for new building constructions is not high enough. Therefore, it should be considered to apply green roof system to existing buildings for resolving various problems. This study evaluates heating and cooling energy consumption based on the combination of passive design factors such as wall, roof, window insulation in addition to a green roof system applied to an existing house by using an energy simulation program. Total 8 potential improvement cases are developed. Each case is applied to the same house with different insulation standard for simulations. Through the analysis of the simulated cases with the chosen test house, it is confirmed that heating energy consumption decreases as improvement cases are applied, but cooling energy consumption is relatively not much affected by each improvement case. In addition, when each improvement case is applied to already highly insulated house, the effect of thermal energy improvement decreases while the same improvement that is applied to the case with low insulated house tends to yield higher improvement rate.

Recent researches on plant factory system deal with the convergence of lighting technology, agricultural technology inclusive to the high-tech industries worldwide in order to respond to the decreasing crop harvest due to global warming and abnormal weather phenomena. However, the fundamental performance standard is not currently being introduced in the case of plants factory and its commercialization is not activated because of high initial investment and operating cost. Large portion of the initial investment and operating cost of a plant factory is ascribed to artificial light sources and thermal control facilities, therefore, innovation should be provided in order to improve the economics of the plant factory. As an alternative, new plant factory could harness solar thermal and geothermal systems for heating, cooling and ventilation. In this study, a natural light dependent multi-layer plant factory`s thermal environment was analyzed with two-dimensional numerical methods to elicit efficient operation conditions for optimized internal physical environment. Depending on the supply air temperature and airflow rate introduced in the facility, the temperature changes around the crops was interpreted. Since the air supplied into the plant factory does not stay long enough, the ambient temperature predicted around the plating trays was not significantly different from that of the supplied air. However, the changes of airflow rate and air flow pattern could cause difference to the temperature around the planting trays. Increasing the amount of time of air staying around the planting trays could improve energy performance in case the thermal environment of a natural light based multi-layer plant factory is considered.

This study analyses the performance characteristics of combined variable ceiling and floor-based air distribution systems in an office room using a numerical method to optimize the level of thermal comfort and indoor air quality. To validate the computational fluid dynamics simulations, temperature, air velocity and CO2 concentration were measured and compared. The simulation results of various combinations of ceiling and floor-based air distribution systems show that the exhaust should be located above the occupant and the window in summer; due to the large thermal plume as heat sources that could be generated by the window and the occupant. Whereas in winter, the supply should be located on the ceiling far from the window and the exhaust should be positioned on the floor near the occupant; as the downward airflow around the cold window surface would become more dominant when compared to the upward airflow associated with any other heat sources. The results have indicated that having both ceiling and floor-based air conditioning systems would allow the flexibility to change supply and exhaust air diffuser locations to optimize thermal and ventilation performance more efficiently in countries that are subject to distinct seasonal changes.

A touch floor system, based on force sensitive resistors, capable of identifying user position and motion with high resolution, is proposed in this paper. A particle swarm optimization-based neural network (NN), initialized with the output of a Levenberg-Marquardt-based NN, allows inaccuracy drawbacks of the trilateration method in position estimation due to sensor's nonlinearity to be reduced to one fifth under non-stationary conditions. Furthermore, position-tracking accuracy is improved by a Kalman filter and a motion recognition algorithm is suggested for mimicking computer mouse clicks. Experimental results show non-uniformly sized icons displayed with high-resolution coordinates can be selected on the floor by the participants of diversified weights. This proves the feasibility of a high-resolution touch floor interface scalable for large area, by facilitating digitally mediated human-architecture interactions.