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Taehoon Hong - One of the best experts on this subject based on the ideXlab platform.

  • a model for predicting the environmental impacts of Educational Facilities in the project planning phase
    Journal of Cleaner Production, 2015
    Co-Authors: Kwangbok Jeong, Changyoon Ji, Taehoon Hong, Hyo Seon Park
    Abstract:

    The environmental impacts of a building are determined in the project planning phase, affecting the whole life cycle of the building. Thus, they should be assessed in the project planning phase so as to reduce the whole environment impacts of the building. This study aims to develop a model for predicting the environmental impacts of a building in the project planning phase, focused on Educational Facilities. This study was conducted in three steps: (i) collection of several information on Educational Facilities and cluster formation using the decision tree; (ii) development of the prediction model using the advanced case-based reasoning; and (iii) evaluation of the environmental impacts of Educational Facilities with six categories. The proposed model was verified compared to the actual data. The error rate for the resource depletion potential was determined to be at 14.14%; global warming potential, 6.80%; ozone-layer depletion potential, 27.29%; acidification potential, 12.94%; eutrophication potential, 18.73%; and photochemical oxidation potential, 43.23%. Due to the limitations of the available information in the project planning phase, it was shown that some impact categories might be estimated with relatively lower accuracy. The proposed model allows an architect or a facility manager to easily and accurately estimate the environmental impacts of the Educational Facilities. It could also be useful for establishing a national environmental policy such as a carbon emissions reduction target. In addition, it could be extended to any other building type or country in the global environment.

  • an estimation model for determining the annual energy cost budget in Educational Facilities using sarima seasonal autoregressive integrated moving average and ann artificial neural network
    Energy, 2014
    Co-Authors: Kwangbok Jeong, Taehoon Hong
    Abstract:

    Electricity consumption in Educational Facilities has increased by an annual average of 9.84% since 2006. However, it is not considered as a factor in determining the AECB (annual energy cost budget) in South Korea. Therefore, this study aims to develop an estimation model for determining the AECB in Educational Facilities using the SARIMA (seasonal autoregressive integrated moving average) model and the ANN (artificial neural network). This study collected electricity consumption data for 7 years (2005–2011) from 787 Educational Facilities. The result of this study showed that the prediction accuracy of the proposed hybrid model (which was developed by combining SARIMA and ANN) was improved, compared to the conventional SARIMA model. The MAPE (mean absolute percentage error) of the proposed method and conventional method for determining the AECB in Educational Facilities was determined at 0.11–0.24% and 1.23–1.84%, respectively. Namely, it was determined that the proposed method was superior to the conventional method. The proposed model could enable executives and managers in charge of budget planning to accurately determine the AECB in Educational Facilities. It could be also applied to other types of resources (e.g., water consumption or gas consumption) used in Educational Facilities.

  • an economic and environmental assessment for selecting the optimum new renewable energy system for Educational facility
    Renewable & Sustainable Energy Reviews, 2014
    Co-Authors: Taehoon Hong, Choongwan Koo, Taehyun Kwak, Hyo Seon Park
    Abstract:

    Abstract With the world's attention focused on climate change, the United Nations Framework Convention on Climate Change provides the basis for global action to encourage sustainable development. A wide variety of measures are being taken in South Korea in line with this trend, but new and renewable energy (NRE) have been highlighted as sustainable energy sources. This study aims to assess the economic and environmental effects of the use of NRE for selecting the optimum NRE system in Educational Facilities. Towards this end, the following were done: (i) selection of facility and its applicable NRE system type; (ii) calculation of the energy generation by the NRE systems via energy simulation; (iii) life cycle cost analysis for the economic evaluation on the NRE systems; (iv) life cycle assessment for the environmental evaluation on the NRE systems; (v) using the net present value and the savings-to-investment ratio, comprehensive evaluation of the economic and environmental effects on the NRE systems. The results of this study can be used to (i) determine which NRE system is most appropriate for Educational Facilities; (ii) calculate the payback period for a certain investment; (iii) decide which location is proper for the implementation of an NRE system considering the characteristics of the regional climate; and (iv) select energy- and cost-efficient elementary schools where the NRE system can be applied.

  • economic and environmental evaluation model for selecting the optimum design of green roof systems in elementary schools
    Environmental Science & Technology, 2012
    Co-Authors: Jimin Kim, Taehoon Hong, Choongwan Koo
    Abstract:

    Green-roof systems offer various benefits to man and nature, such as establishing ecological environments, improving landscape and air quality, and offering pleasant living environments. This study aimed to develop an optimal-scenario selection model that considers both the economic and the environmental effect in applying GRSs to Educational Facilities. The following process was carried out: (i) 15 GRSs scenarios were established by combining three soil and five plant types and (ii) the results of the life cycle CO2 analyses with the GRSs scenarios were converted to an economic value using certified emission reductions (CERs) carbon credits. Life cycle cost (LCC) analyses were performed based on these results. The results showed that when considering only the currently realized economic value, the conventional roof system is superior to the GRSs. However, the LCC analysis that included the environmental value, revealed that compared to the conventional roof system, the following six GRSs scenarios are su...

  • a decision support model for reducing electric energy consumption in elementary school Facilities
    Applied Energy, 2012
    Co-Authors: Taehoon Hong, Kwangbok Jeong
    Abstract:

    The South Korean government has been actively promoting an Educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of Educational Facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of Facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of Educational Facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an Educational-facility improvement program as a decision support model.

Kwangbok Jeong - One of the best experts on this subject based on the ideXlab platform.

  • a model for predicting the environmental impacts of Educational Facilities in the project planning phase
    Journal of Cleaner Production, 2015
    Co-Authors: Kwangbok Jeong, Changyoon Ji, Taehoon Hong, Hyo Seon Park
    Abstract:

    The environmental impacts of a building are determined in the project planning phase, affecting the whole life cycle of the building. Thus, they should be assessed in the project planning phase so as to reduce the whole environment impacts of the building. This study aims to develop a model for predicting the environmental impacts of a building in the project planning phase, focused on Educational Facilities. This study was conducted in three steps: (i) collection of several information on Educational Facilities and cluster formation using the decision tree; (ii) development of the prediction model using the advanced case-based reasoning; and (iii) evaluation of the environmental impacts of Educational Facilities with six categories. The proposed model was verified compared to the actual data. The error rate for the resource depletion potential was determined to be at 14.14%; global warming potential, 6.80%; ozone-layer depletion potential, 27.29%; acidification potential, 12.94%; eutrophication potential, 18.73%; and photochemical oxidation potential, 43.23%. Due to the limitations of the available information in the project planning phase, it was shown that some impact categories might be estimated with relatively lower accuracy. The proposed model allows an architect or a facility manager to easily and accurately estimate the environmental impacts of the Educational Facilities. It could also be useful for establishing a national environmental policy such as a carbon emissions reduction target. In addition, it could be extended to any other building type or country in the global environment.

  • an estimation model for determining the annual energy cost budget in Educational Facilities using sarima seasonal autoregressive integrated moving average and ann artificial neural network
    Energy, 2014
    Co-Authors: Kwangbok Jeong, Taehoon Hong
    Abstract:

    Electricity consumption in Educational Facilities has increased by an annual average of 9.84% since 2006. However, it is not considered as a factor in determining the AECB (annual energy cost budget) in South Korea. Therefore, this study aims to develop an estimation model for determining the AECB in Educational Facilities using the SARIMA (seasonal autoregressive integrated moving average) model and the ANN (artificial neural network). This study collected electricity consumption data for 7 years (2005–2011) from 787 Educational Facilities. The result of this study showed that the prediction accuracy of the proposed hybrid model (which was developed by combining SARIMA and ANN) was improved, compared to the conventional SARIMA model. The MAPE (mean absolute percentage error) of the proposed method and conventional method for determining the AECB in Educational Facilities was determined at 0.11–0.24% and 1.23–1.84%, respectively. Namely, it was determined that the proposed method was superior to the conventional method. The proposed model could enable executives and managers in charge of budget planning to accurately determine the AECB in Educational Facilities. It could be also applied to other types of resources (e.g., water consumption or gas consumption) used in Educational Facilities.

  • a decision support model for reducing electric energy consumption in elementary school Facilities
    Applied Energy, 2012
    Co-Authors: Taehoon Hong, Kwangbok Jeong
    Abstract:

    The South Korean government has been actively promoting an Educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of Educational Facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of Facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of Educational Facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an Educational-facility improvement program as a decision support model.

Hyo Seon Park - One of the best experts on this subject based on the ideXlab platform.

  • a model for predicting the environmental impacts of Educational Facilities in the project planning phase
    Journal of Cleaner Production, 2015
    Co-Authors: Kwangbok Jeong, Changyoon Ji, Taehoon Hong, Hyo Seon Park
    Abstract:

    The environmental impacts of a building are determined in the project planning phase, affecting the whole life cycle of the building. Thus, they should be assessed in the project planning phase so as to reduce the whole environment impacts of the building. This study aims to develop a model for predicting the environmental impacts of a building in the project planning phase, focused on Educational Facilities. This study was conducted in three steps: (i) collection of several information on Educational Facilities and cluster formation using the decision tree; (ii) development of the prediction model using the advanced case-based reasoning; and (iii) evaluation of the environmental impacts of Educational Facilities with six categories. The proposed model was verified compared to the actual data. The error rate for the resource depletion potential was determined to be at 14.14%; global warming potential, 6.80%; ozone-layer depletion potential, 27.29%; acidification potential, 12.94%; eutrophication potential, 18.73%; and photochemical oxidation potential, 43.23%. Due to the limitations of the available information in the project planning phase, it was shown that some impact categories might be estimated with relatively lower accuracy. The proposed model allows an architect or a facility manager to easily and accurately estimate the environmental impacts of the Educational Facilities. It could also be useful for establishing a national environmental policy such as a carbon emissions reduction target. In addition, it could be extended to any other building type or country in the global environment.

  • an economic and environmental assessment for selecting the optimum new renewable energy system for Educational facility
    Renewable & Sustainable Energy Reviews, 2014
    Co-Authors: Taehoon Hong, Choongwan Koo, Taehyun Kwak, Hyo Seon Park
    Abstract:

    Abstract With the world's attention focused on climate change, the United Nations Framework Convention on Climate Change provides the basis for global action to encourage sustainable development. A wide variety of measures are being taken in South Korea in line with this trend, but new and renewable energy (NRE) have been highlighted as sustainable energy sources. This study aims to assess the economic and environmental effects of the use of NRE for selecting the optimum NRE system in Educational Facilities. Towards this end, the following were done: (i) selection of facility and its applicable NRE system type; (ii) calculation of the energy generation by the NRE systems via energy simulation; (iii) life cycle cost analysis for the economic evaluation on the NRE systems; (iv) life cycle assessment for the environmental evaluation on the NRE systems; (v) using the net present value and the savings-to-investment ratio, comprehensive evaluation of the economic and environmental effects on the NRE systems. The results of this study can be used to (i) determine which NRE system is most appropriate for Educational Facilities; (ii) calculate the payback period for a certain investment; (iii) decide which location is proper for the implementation of an NRE system considering the characteristics of the regional climate; and (iv) select energy- and cost-efficient elementary schools where the NRE system can be applied.

Georgios Dafoulas - One of the best experts on this subject based on the ideXlab platform.

  • architectural propositions for enhancement of learning spaces within 3d virtual learning environments
    International Conference on Information Society, 2010
    Co-Authors: Noha Saleeb, Georgios Dafoulas
    Abstract:

    Newly emergent 3D Virtual Learning Environments (VLEs), e.g. Second Life, are increasingly being utilised by many Educational institutions and universities to deliver e-learning. This necessitates erection of virtual campuses to accommodate classes and sessions conducted within these worlds. However, sparse research exists that explores users' satisfaction from buildings used within these 3DVLEs. Furthermore, no research exists that discusses contentment levels of users specifically towards 3D Educational Facilities, or users' preferences and requirements from buildings' different constructional and architectural design elements. This research investigates the presence of such impact of architectural features of 3D virtual Educational buildings and classrooms on users' comfort within them, by recording, analyzing and categorizing higher education students' and staff's design preferences and propositions to enhance virtual campus' learning spaces, internally and externally. This has potential to boost e-learning experiences within 3DVLEs analogous to the positive effect of physical real-life architecture on students' learning within their respective classrooms.

  • analogy between impact of architectural design characteristics of learning spaces on learners in the physical world and 3d virtual world
    2010
    Co-Authors: Noha Saleeb, Georgios Dafoulas
    Abstract:

    This research starts by establishing from literature the importance of architectural design elements of physical learning spaces on face-to-face learning, hence, after illustrating examples of different types of architecture in Second Life, delves into exploring the effect of individual architectural features of 3D virtual building design, such as color, shape of class, lighting and open spaces, height of space, textures and other aspects on higher education learners during online e-learning sessions conducted in virtual worlds, in an analogy with the physical world. Learners are divided into three groups: (i) under-graduate students, (ii) post-graduate students, and (iii) adult learners and researchers. Results comprising charts and diagrams capturing, using surveys, the extent of learners’ satisfaction from being inside different 3D virtual university campuses in Second Life, representing different variations of architectural design elements in each learning space, hence their contentment from specific design characteristics, preferences and suggestions for design of a better learning environment are demonstrated. Moreover, this presentation will reveal how this research can help initiate the development of a framework or recommendations for building codes, for Educational Facilities within 3D Virtual Environments, to complement or contradict existing codes for erecting such Facilities in the physical real-life world.

  • whose turn to renovate the class today analogy between impact of architectural design characteristics of learning spaces on learners in the physical world and 3d virtual world
    2010
    Co-Authors: Noha Saleeb, Georgios Dafoulas
    Abstract:

    This research starts by establishing from literature the importance of architectural design elements of physical learning spaces on face-to-face learning, hence, after illustrating examples of different types of architecture in Second Life, delves into exploring the effect of individual architectural features of 3D virtual building design, such as color, shape of class, lighting and open spaces, height of space, textures and other aspects on higher education learners during online e-learning sessions conducted in virtual worlds, in an analogy with the physical world. Learners are divided into three groups: (i) under-graduate students, (ii) post-graduate students, and (iii) adult learners and researchers. Results comprising charts and diagrams capturing, using surveys, the extent of learners’ satisfaction from being inside different 3D virtual university campuses in Second Life, representing different variations of architectural design elements in each learning space, hence their contentment from specific design characteristics, preferences and suggestions for design of a better learning environment are demonstrated. Moreover, this presentation will reveal how this research can help initiate the development of a framework or recommendations for building codes, for Educational Facilities within 3D Virtual Environments, to complement or contradict existing codes for erecting such Facilities in the physical real-life world.

Gary W Mullins - One of the best experts on this subject based on the ideXlab platform.

  • model of affective learning for nonformal science education Facilities
    Journal of Research in Science Teaching, 1997
    Co-Authors: Joyce E Meredith, Rosanne W Fortner, Gary W Mullins
    Abstract:

    Objective setting and evaluation for learning in the affective domain are often neglected in Educational programs, largely because affective learning is a poorly understood phenomenon. This is particularly problematic in nonformal science education Facilities, which are uniquely suited to facilitate affective learning. To address this problem, a heuristic model of affective learning in nonformal Educational Facilities was developed. The model, referred to as the Meredith Model, displays a sequence of events occurring in the affective responses of learners in nonformal Educational experiences and identifies factors which may influence individual events within this sequence. The model is proposed as a conceptual framework for gaining an increased understanding of affective learning and for making recommendations for practice of nonformal science education and for further research. J Res Sci Teach 34: 805–818, 1997.