• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.81-82
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• 2010

Emerging global economic growth and increasing demand for energy supply and demand imbalance and the excessive use of fossil fuels existing the rapidly increasing greenhouse gas emissions and resource depletion of global energy crisis is deepening. Accordingly, improvement of living conditions around and through the natural ecological preservation and the need for a comfortable life for the meeting the importance of energy management and consumption are emerging. Many in the field of architecture for energy-saving measures, and conducting research and verify green building energy ratings and low energy for the initial steps that can be verified from the Energy Performance of BIM(Building Information Model) technology development and commercialization of the building energy to predict the performance objectively, leverages technology in an existing building energy performance analysis and possibilities of BIM-based green building process presented. In this study, using BIM for existing building energy performance analysis of data collected through the objective and efficient management of the energy it consumes Mapping and Management Plan is to research on.

• Journal of Environmental Impact Assessment
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• v.28 no.6
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• pp.592-603
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• 2019

This study is about the implementation of low energy sewage management technology through effective control of blower which consumes the most energy in sewage treatment. In calculating the amount of oxygen required for microorganisms, unlike the existing method using the operating index in the bioreactor or TMS data in the discharge port, the CODcr and NH₄⁺-N concentration changes in sewage flowing into the sewage treatment plant were detected in advance before entering the bioreactor and the amount of air was controlled based on this. The pre-sensing was found to have a high correlation compared with conventional products. As a result of blower control, it was possible to save about 9.9% energy more than the manual control. Consequently, this study suggested the possibility of blower's real-time control combined with pre-sensing technology. Also, it is expected that the low energy sewage treatment can be applied to sewage treatment facilities dependent on operation by manpower, and it will contribute to the reduction of greenhouse gas emissions.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.