• Economic and Environmental Geology
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• v.23 no.4
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• pp.383-392
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• 1990

This study found that that dynamics of intensity of use and economic theory of derived demand can both be accommodated through an extensive translog demand model. The basic idea in this recognition is that the skewed life cycle empirical pattern of intensity of use plotted against per capita income is of lognormal form and this lognomal intensity of use model can be mathematically transformed into an eqivalent simple translog intensity of use model. Empirical results showed that this extensive traslog model, which is a flexible function and includes both the classical case of fixed coefficients and the dynamic case of varying coefficients of the explanatory variables, gave better forecasts than the original intensity of use model and other conventional models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1019-1023
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• 2018

In this paper sensitivity analysis of temperature on special day electricity demand of land and Jeju Island is performed. The basic electricity demand per 3 hours is defined as electricity demand that reflects the GDP effect without the temperature influence. The temperature sensitivity per 3 hours is calculated through the relationship between special day electricity demand normalized to basic electricity demand and temperature. In the future, forecast error will be improved if the temperature sensitivity per 3 hours is applied to the special day load forecasting.

• Wind and Structures
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• v.20 no.1
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• pp.59-74
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• 2015

A coupled model system for Wind Resource Assessment (WRA) was studied. Using a mesoscale meteorological model, the Weather Research and Forecasting (WRF) model, global-scale data were downscaled to the inner nested grid scale (typically a few kilometers), and then through the coupling Computational Fluid Dynamics (CFD) mode, FLUENT. High-resolution results (50 m in the horizontal direction; 10 m in the vertical direction below 150 m) of the wind speed distribution data and ultimately refined wind farm information, were obtained. The refined WRF/FLUENT system was then applied to assess the wind resource over complex terrain in the northern Poyang Lake region. The results showed that the approach is viable for the assessment of wind energy.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.36.1-36.1
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• 2011

It is generally believed that eruptive phenomena in the solar atmosphere such as solar flares and coronal mass ejections (CMEs) occur in the solar active regions with complex magnetic structures. Magnetic helicity has been recognized as a useful parameter to measure the complexity such as twists, kinks, and inter-linkages of magnetic field lines. The objective of this study is to understand a long-term (a few days) variation of magnetic helicity in active regions and its relationship with the energy buildup and instability leading to flares and CMEs. Statistical studies of flare productivity and magnetic helicity injection in about 400 active regions were carried out. The temporal variation of magnetic helicity injected through the photosphere of active regions was also examined related to 46 CMEs. The main findings in this study are as follows: (1) the study of magnetic helicity for active regions producing major flares and CMEs indicates that there is always a significant helicity injection through the active-region photosphere over a long period of 0.5 - a few days before the flares and CMEs; (2) for the 30 CMEs under investigation, it is found that there is a fairly good correlation (linear correlation coefficient of 0.71) between the average helicity injection in the CME-productive active regions and the CME speed. Beside the scientific contribution, a major impact of this study is the observational discovery of a characteristic variation pattern of magnetic helicity injection in flare/CME-productive active regions which can be used for the improvement of solar eruption forecasting.

• Journal of Environmental Impact Assessment
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• v.22 no.4
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• pp.329-343
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• 2013

According to previous studies, the increased air temperature can lead to change of thermal stratification structure of lakes and reservoirs. The changed thermal stratification may result in alteration of materials and energy flow. The objective of this study was to predict the effect of climate change on the water temperature and stratification structure of Daecheong Reservoir, located in Geum River basin of Korea, using a three-dimensional(3D) hydrodynamic model(ELCOM). A long-term(100 years) weather data set provided by the National Institute of Meteorological Research(NIMR) was used for forcing the 3D model. The model was applied to two different hydrological conditions, dry year(2001) and normal year(2004). It means that the effect of air temperature increase was only considered. Simulation results showed that the surface water temperature of the reservoir tend to increase in the future, and the establishment of thermal stratification can occur earlier and prolonged longer. As a result of heat flux analysis, the evaporative heat loss can increase in the future than now and before. However, the convective heat loss and net long wave radiation from water surface decreased due to increased air temperature.

• Korean System Dynamics Review
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• v.6 no.1
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• pp.99-123
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• 2005

Although the generation market is competitive, the power market is easily exercised the market power by one generator due to its special futures such as a limited supplier, large investment cost, transmission constraints and loss. Specially, as Korea Electric industry restructuring is similar US competitive wholesale electricity market structure which discovered the several evidences of market power abuse, when restructuring is completed the possibility that market power will be exercised is big. Market power interferes with market competitions and efficiency of system. The goal of this study is to investigate the market price effects of the potential market power and the proposed market power mitigation strategy in Korean market using the forecasting wholesale electricity market model. This modeling is developed based on the system dynamics approach. it can analyze the dynamic behaviors of wholesale prices in Korean market. And then it is expanded to include the effect of market condition changed by 'strategic behavior' and 'real time pricing.' This model can generate the overall insights regarding the dynamic impact of output withholding by old gas fire power plant bon as a marginal plant in Korean market at the macro level. Also it will give the energy planner the opportunity to create different scenarios for the future for deregulated wholesales market in Korea.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.475-487
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• 2020

The paper aims to facilitate a discussion around how big data technologies and data from citizens can be used to help public administration, society, and policy-making to improve community's lives. This paper discusses opportunities and challenges of big data strategies for government, society, and policy-making. It employs the presentation of numerous practical examples from different parts of the world, where public-service delivery has seen transformation and where initiatives have been taken forward that have revolutionized the way governments at different levels engage with the citizens, and how governments and civil society have adopted evidence-driven policy-making through innovative and efficient use of big data analytics. The examples include the governments of the United States, China, the United Kingdom, and India, and different levels of government agencies in the public services of fraud detection, financial market analysis, healthcare and public health, government oversight, education, crime fighting, environmental protection, energy exploration, agriculture, weather forecasting, and ecosystem management. The examples also include smart cities in Korea, China, Japan, India, Canada, Singapore, the United Kingdom, and the European Union. This paper makes some recommendations about how big data strategies transform the government and public services to become more citizen-centric, responsive, accountable and transparent.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.244-252
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• 2021

Wind plays an important role in cases of unexpected radioactive pollutant dispersion, deciding distribution and concentration of the leaked substance. The accurate prediction of wind has been challenging in numerical weather prediction models, especially near the surface because of the complex interaction between turbulent flow and topographic effect. In this study, we investigated the characteristics of atmospheric dispersion of radioactive material (i.e. ¹³⁷Cs) according to the simulated boundary layer around the HANARO research nuclear reactor in Korea using the Weather Research and Forecasting (WRF)-Mesoscale Model Interface (MMIF)-California Puff (CALPUFF) model system. We examined the impacts of orographic drag on wind field, stability calculation methods, and planetary boundary layer parameterizations on the dispersion of radioactive material under a radioactive leaking scenario. We found that inclusion of the orographic drag effect in the WRF model improved the wind prediction most significantly over the complex terrain area, leading the model system to estimate the radioactive concentration near the reactor more conservatively. We also emphasized the importance of the stability calculation method and employing the skillful boundary layer parameterization to ensure more accurate low atmospheric conditions, in order to simulate more feasible spatial distribution of the radioactive dispersion in leaking scenarios.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.