• Journal of Korea Planning Association
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• v.54 no.4
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• pp.122-130
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• 2019

The purpose of this study is to analyze effect of climate and urban factors on energy resilience, and to explore policy alternatives to strengthen resilience of energy system. For this purpose, this study used extensive literature review on resilience studies and multiple regression analysis. In this study, blackout time was set as a dependent variable. And the independent variables were divided into climate and urban (robustness, countermeasure capacity) characteristics. As a result of the analysis, in terms of climate characteristics, maximum wind speed and cooling/heating degree-day have statistically significant impact on blackout time. With regard to urban characteristics, number of consumer, ratio of deteriorated housing and coast dummy variables have statistically significant impact on blackout time. And the ratio of government employees and road ratio were found to be the most influencing factors to shorten time taken to restore original level of electricity supply. Based on the study results, several policy suggestions to improve energy resilience were made such as continuous management of vulnerable areas and strengthening disaster response services. This study only considered engineering dimension of resilience. Further studies need to be approached on ecological & social-ecological dimension.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

In order to increase the use of electric vehicles (EVs) and minimize grid strain, microgrid using renewable energy must take an important role. Microgrid may use fossil fuels such as small diesel power, but in many cases, they can be supplied with energy from renewable energy, which is an eco-friendly energy source. However, renewable energy such as solar and wind power have variable output characteristics. Therefore, in order to meet the charging and discharging energy demands of electric vehicles and at the same time supply load power stably, it is necessary to review the configuration of electric vehicle charging infrastructure that utilizes diesel power or electric vehicle-to-grid (V2G) as a parallel energy source in the microgrid. Against this background, this study modelized a microgrid that can stably supply power to loads using solar power, wind power, diesel power, and V2G. The proposed microgrid uses solar power and wind power generation as the primary supply energy source to respond to power demand, and determines the operation type of the load's electric vehicles and the rotation speed of the load synchronous machine to provide stable power from diesel power for insufficient generations. In order to verify the system performance of the proposed model, we studied the stable operation plan of the microgrid by simulating it with MATLAB /Simulink.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1085-1092
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• 2009

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1293-1300
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• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.445-452
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• 2017

Electric pole is a supporting beam used for power transmission/distribution which accelerometer are used for measuring a external force. The meteorological condition has various effects on the external forces of electric pole. One of them is the elasticity change of the aerial wire. It is very important to perform modelling. The acceleration sensor is converted into a pitch and a roll angle. The meteorological condition has a high correlation between variables, and selecting significant explanatory variables for modeling may result in the problem of over-fitting. We constructed high deviance explained model considering multicollinearity using the Generalized Additive Model which is one of the machine learning methods. As a result of the Variation Inflation Factor Test, we selected and fitted the significant variable as temperature, precipitation, wind speed, wind direction, air pressure, dewpoint, hours of daylight and cloud cover. It was noted that the Hours of daylight, cloud cover and air pressure has high explained value in explonatory variable. The average coefficient of determination (R-Squared) of the Generalized Additive Model was 0.69. The constructed model can help to predict the influence on the external forces of electric pole, and contribute to the purpose of securing safety on utility pole.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.811-819
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• 2013

This study aims to analyze determinants of mountainous and coastal area's housing value caused by the characteristics of the natural environment. As the current issue of housing value is throwing the spotlight on the climate change recently, environmental features are significantly important than before. There were a lot of studies on the influence of environmental characteristics to the housing price but these studies were mostly dealing with the housing price in especially apartments nearby Han-river in Seoul, South Korea. To have differences with existing studies, environmental characteristics estimating housing value are classified as 8 elements including the view, the wind speed, and the humidity. The result of this study is in following; there were few significant environmental variables in mountainous housing value growth model. This means people living in mountainous area recognize on environmental factors more such as housing or complex characteristics. People living in coastal area are much more sensitive environment variables in their residence value than mountainous area. Especially, the view for the ocean is the most important variable in housing value, and wind speed is second positively significant. Humidity and safety of disaster are negatively significant variables.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Journal of Climate Change Research
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• v.3 no.2
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• pp.153-159
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• 2012

CFC-12 used in mobile air conditioning(MAC) system has been replaced by R-134a, a type of HFC refrigerant, from 1991 to 1994. R-134a has since been widely used as a refrigerant of a mobile air conditioner. However, it is one of the six main green house gases listed in Kyoto Protocol, which makes it imperative to regulate its emission and develop alternative refrigerants. In this study, the concentration of leaked R-134a was measured using VT(Variable Temperature) shed and Running loss test shed to analyze the level of air conditioner refrigerant leaked in a vehicle. According to the analysis of the concentration of R-134a leaked from a vehicle parked, annual leakage amount of R-134a was in the range of 6.46~13.28 g/yr. The figure was similar with the leakage from the mobile air conditioning system currently used. In a study using the same vehicle model, a vehicle equipped with dual evaporation system had a higher leakage rate of refrigerant than a vehicle with a single evaporation system. It appears that the added fittings and joints of the dual evaporator system led to higher leakage rate. Besides, the analysis of the change in R-134a concentration under various car speed found that more refrigerant leaked under high speed(100km/hr) and but the volume of the wind did not affect to the variation of refrigerant leakage.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.656-664
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• 2018

The following study was conducted to determine the optimal location in terms of efficiency for solar energy facilities, and to propose a policy implications for the orientation of the installments. 92 cases in Jeollanam-do Province were selected. A regression analysis was performed between the average electricity generation time as the dependent variable, and the facility, weather and site conditions as the independent variables. As a result, 5 variables were deemed significant. Larger site areas, closer proximity to rivers, islands, oceans, etc., least south-oriented, higher average wind speed, and facilities in agricultural land use and natural environment conservation land use had the highest efficiency. This study minimized the possibility of secure databases and errors following facility types, and was limited in the number of sites studied, since this was only conducted in Jeollanam-do Province. Nevertheless, these conclusions still offer important policy implications for determining the most optimal location for solar energy facilities.

• KIEE International Transactions on Power Engineering
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• v.3A no.1
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• pp.27-34
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• 2003

In this paper, the power conditioner composed of the stand-alone single-phase squirrel cage rotor type self-excited induction generator (SEIG) driven by prime movers such as a wind turbine and a micro gas turbine (MGT) is presented by using the steady-state circuit analysis based on the two nodal admittance approaches using the per-unit frequency in addition to a new state variable defined by the per-unit slip frequency along with its performance evaluations for the stand-alone energy utilizations. The stande-alone single-phase SEIG operating performances in unregulated voltage control loop are then evaluated on line under the conditions of the speed change transients of the prime mover and the stand-alone electrical passive load power variations with the simple theoretical analysis and the efficient computation processing procedures described in the part I of this paper. In addition, a feasuible PI controlled feedback closed-loop voltage regulation scheme of the stande-alone single-phase SEIG is designed on the basis of the static VAR compensate. (SVC) and discussed in experiment for the promising stand-alone power conditioner. The experimental operating performance results are illustrated and give good agreements with the simulation ones. The simulation and experimental results of the stand-alone single-phase SEIG with the simple SVC controller for its stabilized voltage regulation prove the practical effectiveness of the additional SVC control loop scheme including the PI controller with fast response characteristics and steady-sate performance improvements.