• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.261-269
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• 2023

During a nuclear power plant (NPP) accident, radioactive material may be released into the surrounding environment in the form of a radioactive plume. The behavior of the radioactive plume is influenced by meteorological factors such as wind direction and speed. If the residents are evacuated to a shelter in the direction of the flow of the radioactive plume, the radiation exposure of the residents may increase, contrary to the purpose of the evacuation. To avoid such an undesirable outcome, this paper applies a big data analysis to evaluate the suitability of the shelter locations near 5 NPPs in the Republic of Korea in terms of the seasonal wind direction frequency in those areas. To this end, the wind data measured around the NPPs from 2016 to 2020 were analyzed to derive the seasonal wind direction frequency using a big data analysis. These analyses results were then used to determine how many shelters around NPPs locate in areas with prevailing wind direction per season. Then, suggestions were made on the direction for residents not to evacuate, if possible, that is, the prevailing seasonal wind directions for 5 NPPs, depending on the season in which the accident occurs.

• Journal of Climate Change Research
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• v.34 no.14
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• pp.5621-5634
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• 2021

The life cycle of El Niño-Southern Oscillation (ENSO) typically follows a seasonal march, with onset in spring, developing during summer, maturing in boreal winter, and decaying over the following spring. This feature is referred to as ENSO phase locking. Recent studies have noted that seasonal modulation of the ENSO growth rate is essential for this process. This study investigates the fundamental effect of a seasonally varying growth rate on ENSO phase locking using a modified seasonally dependent recharge oscillator model. There are two phase locking regimes associated with the strength of the seasonal modulation of growth rate: 1) a weak regime in which only a single peak occurs and 2) a strong regime in which two types of events occur either with a single peak or with a double peak. Notably, there is a seasonal gap in the strong regime, during which the ENSO peak cannot occur because of large-scale ocean-atmosphere coupled processes. We also retrieve a simple analytical solution of the seasonal variance of ENSO, revealing that the variance is governed by the time integral of seasonally varying growth rate. Based on this formulation, we propose a seasonal energy index (SEI) that explains the seasonal gap and provides an intuitive explanation for ENSO phase locking, potentially applicable to global climate model ENSO diagnostics.

• Journal of the Korean Solar Energy Society
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• v.40 no.5
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• pp.47-58
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• 2020

Ministry of Land, Infrastructure and Transport(MOLIT) has increased reduction rate from 18.1% to 32.7% in Building sector compared to BAU of the national greenhouse gas emission according to the 2030 Greenhouse Gas Reduction Road map Amendment. For this purpose, MOLIT has been activating the green remodeling projects for existing buildings. Considering that 15 year old buildings after completion are 74% (5.25 million buildings) among about 7 million existing building stocks in Korea, reduction of building energy consumption by green remodeling is urgently needed, However, it is a major difficulty of activation for green remodeling projects because there are few case studies on Before and After building energy consumption of actual green remodeling projects. Considering that building energy performance and value increase after green remodeling through previous researches, additional studies of the energy consumption assessment on actual green remodeling projects are essential. Therefore, this study aims to propose results on Before and After building energy consumption of actual green remodeling projects.

• Journal of the Korea Organic Resources Recycling Association
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• v.18 no.4
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• pp.54-63
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• 2010

When MBT(Mechanical Biological Treatment) facility is designed, the management system adequate for domestic circumstance in Korea has been insufficient and power plant's load on seasonal variation has not been resolved yet. Thus, this study introduced MBT facility and MSW(Municipal Solid Waste)'s seasonal emission characteristics were investigated in order to establish new MBT management system. and additional thermal buffer-materials's calorific values were also considered to reduce the power plant's load. The results showed that the screening efficiency of MBT facility and the physical characteristics of each waste can be identified, and the calorific value by seasonal variation for MBT facility can be kept constant all the year round by using an additional thermal buffer-materials.

• Asian Journal of Atmospheric Environment
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• v.6 no.1
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• pp.23-32
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• 2012

The ionic composition of $PM_{2.5}$ samples was investigated by their datasets of cationic ($Na^+$, $NH_4^+$, $K^+$, $Mg^{2+}$, and $Ca^{2+}$) and anionic components ($Cl^-$, $NO_3^-$, and $SO_4^{2-}$) along with relevant environmental parameters collected from an urban monitoring site in Korea at hourly intervals in 2010. The mean (and SD) annual concentration of $PM_{2.5}$ was computed as 25.3 ${\mu}g\;m^{-3}$ with the wintertime maximum. In addition, sum concentrations (neq $m^{-3}$) of five cationic species (291) were slightly lower than 3 anionic species (308). Most cations exhibited the highest seasonal values in spring, while anions showed more diversified seasonal patterns. According to PCA, five major source categories were apparent with the relative dominance of secondary inorganic aerosols (SIA). The results of our study suggest consistently that the distribution of ionic constituents in an urban area is affected by the combined effects of both natural and anthropogenic processes.

• Geomechanics and Engineering
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• v.16 no.5
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• pp.503-512
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• 2018

Globally there is an increasing need to reduce the greenhouse gas emissions and increase the use of renewable sources of energy. The storage of solar thermal energy is a crucial aspect for implementing the solar energy for space heating in high latitudes, where solar insolation is high in summer and almost negligible in winter when the domestic heating demand is high. To use the solar heating during winter thermal energy storage is required. In this paper, equations representing the single U-tube heat exchanger are implemented in weak form edge elements in COMSOL Multiphysics(R) to speed up the calculation process for modelling of a borehole storage layout. Multiple borehole seasonal solar thermal energy storage scenarios are successfully simulated. After 5 years of operation, the most efficient simulated borehole pattern containing 168 borehole heat exchangers recovers 69% of the stored seasonal thermal energy and provides 971 MWh of thermal energy for heating in winter.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.14 no.3
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• pp.1-7
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• 2018

In this paper, we designed a seasonal geothermal storage system and studied the applicability in the alluvial aquifer. We conducted a basic survey to apply this system to greenhouses actually operated in the Geum river basin alluvial aquifer. After choosing a potential area through electrical resistivity survey, the system parameters were set using drilling survey and pumping test result. We installed a system based on the factors, and operated for about 9 months. As a result, high temperature water(injection temperature $30^{\circ}C$) was stored at 22.5 Mcal ($1,609m^3$) for 3 months in cooling operation and 125 Mcal ($16,960m^3$) of low temperature water (injection temperature $7^{\circ}C$) were stored for 6 months in the remaining heating operation.

• Journal of the Korean Solar Energy Society
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• v.31 no.4
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• pp.66-71
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• 2011

This study focused on the application of the Passive Solar Chamber System (PSCS) as proposed by a previous study. The seasonal performance and sizing method for the system were investigated for a feasibility of the PSCS in Korean climate. For seasonal performance, heat and ventilation performances of the PSCS were analyzed for the months of January and August. This study proposed a simple configuration method in which the designer can decide on the system size at the preliminary design stage by using system efficiency, overall heat transfer coefficient transmission, monthly solar radiation, highest and lowest temperatures. During weeks that require heating, the system showed to acquire a daily average heat amount of $860.28Wh/m^2$ day. For cooling periods, the system was computed to supply a daily average natural ventilation of $1,360.2m^3/day$ to the room. Moreover, proposed sizing method and the overall computation results showed a 6.04~7.24% error of assessment.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.5
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• pp.51-58
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• 2018

The purpose of this study was to analyze the effect of seasonal change in characteristics of hygiene activity on domestic hot water energy consumption. With 16 residents of 4 households, the data about frequency of hygiene activity and water temperature was collected from February to August, 2017. The results of collected data discovered that the frequency of hygiene activity was higher especially in summer, whereas the consumption of warm water they used was higher in winter. The seasonal change in characteristics of hygiene activity was analyzed to be changed and strongly influenced by outdoor temperature. The influence of characteristics of hygiene activity on hot water consumption was analyzed. There was 13% of difference between consumption that was calculated taking characteristics of hygiene activity into account and consumption that was not. Therefore, this study suggested hygiene activity schedule, hot water profile and hot water consumption pattern, which can be utilized for improving simulation as well.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.