• KEPCO Journal on Electric Power and Energy
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• v.3 no.1
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• pp.65-72
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• 2017

This study analyzed causal relationship between electricity consumption and economic output (GDP) for Korea from 2001 to 2014 employing the vector error-correction model estimation by manufacturing sector. The results of unit-roots tests show that all sectoral GDP and electricity consumptions were not stationary. And cointegration tests show that processed foods, Wood Pulp Paper, electricity apparatus, Precision Medical sectors had a linear combinations in the long run between electricity consumptions and economic growth. With respect to the direction of causality, manufacturing sector has a uni-directional running from economic output (GDP) to electricity consumption in short term. The results of study show that sectoral causal relation were different each other in short term and long term. These findings imply that electricity demand management policy focusing on efficiency improvement is necessary to minimize negative impacts on economic growth and to adopt suitable structural policies can induce energy conservation.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.723-734
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• 2021

Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.100-103
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• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.7-16
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• 2004

The purpose of this study was to find the characteristics of travel behavior and accessibility in terms of spatial structure. We analyzed travel behaviors and accessibility using a mode choice model and the Complementary Accessibility Index(CAI). The urban spatial structures that were compared were a compact city (CC) versus a sprawled city (SC), and high residential density districts (HD) versus low residential density districts (LD). First, CC and HDs residents had a shorter commuting distance than the CC and LDs residents. Second, behavior models showed that the use of Private cars for commuting in SCs was found to be greater than private car use in CCs, and that public transportation modes would be encouraged in CCs. Third, changes associated with the time and cost of commuting by private car generally affect the demand for public transportation modes in the CC. Also, analysis of cross elasticity suggests that changes of subway travel time affect the demand for buses very elastically. Fourth, the CAI of SC and LD were superior to the CC and HD even though the SC inefficient urban forms in terms of spatial structure. So, the spatial distribution of population density was also found to be an important factor affecting accessibility and energy savings.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1373-1383
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• 2016

The wind energy industry and wind power generation have increased; consequently, the stable supply of the wind power has become an important issue. It is important to accurately predict the wind power with short-term basis in order to make a reliable planning for the power supply and demand of wind power. In this paper, we first analyzed the speed, power and the directions of the wind. The neural network and the time series models (ARMA, ARMAX, ARMA-GARCH, Holt Winters) for wind power generation forecasting were compared based on mean absolute error (MAE). For one to three hour-ahead forecast, ARMA-GARCH model was outperformed, and the neural network method showed a better performance in the six hour-ahead forecast.

• International Journal of Automotive Technology
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• v.8 no.6
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• pp.761-769
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• 2007

The configuration of the flow channel on a bipolar plate of a proton exchange membrane fuel cell(PEMFC) for efficient reactant supply has great influence on the performance of the fuel cell. Recent demand for higher energy density fuel cells requires an increase in current density at mid voltage range and a decrease in concentration overvoltage at high current density. Therefore, an interdigitated flow channel where mass transfer rate by convection through a gas diffusion layer is greater than the mass transfer by a diffusion mechanism through a gas diffusion layer was recently proposed. This study attempts to analyze the i-V performance, mass transfer and pressure drop in interdigitated flow channels by developing a fully three dimensional simulation model for PEMFC that can deal with anode and cathode flow together. The results indicate that the trade off between performance and pressure loss should be considered for efficient design of flow channels. Although the performance of the fuel cell with interdigitated flow is better than that with conventional flow channels due to a strong mass transfer rate by convection across a gas diffusion layer, there is also an increase in friction due to the strong convection through the porous diffusion layer accompanied by a larger pressure drop along the flow channel. It was evident that the proper selection of the ratio of channel and rib width under counter flow conditions in the fuel cell with interdigitated flow are necessary to optimize the interdigitated flow field design.

• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.53-60
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• 2010

LPG(Liquefied Petroleum Gas) vehicles in metropolitan area are being applied to improve air quality and have been proven effective for the reduction of air pollutant. In addition, LPG demand is growing rapidly as an environmentally friendly energy source and its gas station is also increasing every year. Consequently, this study tries to find out the influence of flame caused by the VCE(Vapor Cloud Explosion) in filling station on the adjacent combustibles and people by simulating relevant quantity of TNT. In addition, the damage estimation was conducted by using API regulations. If the scale of the radiation heat is known by calculating the distance of flame influence from the explosion site, the damage from the site can be easily estimated. And the accident damage was estimated by applying the influence on the adjacent structures and people into the PROBIT model. According to the probit analyze, the spot which is 30m away from the flame has 100% of the damage probability by the first-degree burn, 99.2% of the damage probability by the second-degree burn and 93.4% of the death probability by the fire.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.6
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• pp.457-465
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• 2009

Lithium batteries are widely used in mobile electronic devices due to their higher voltage and energy density, lighter weight and longer life cycle compared to other secondary batteries. In particular, high demand for lithium batteries is expected for electric cars. In case of lithium batteries used in electric cars, driving distance must be calculated accurately and discharging should not be done below the level of making it impossible to crank. Therefore, accurate information about state of charge (SOC) becomes an essential element for reliable driving. In this paper, a new method of estimating the SOC of lithium polymer batteries by using AC impedance is proposed. In the proposed method, parameters are extracted by fitting a curve of impedance measured at each frequency on the equivalent impedance model and extracted parameters are used to estimate SOC. Experiments were conducted on lithium polymer batteries with similar capacities made by different manufacturers to prove the validity of the proposed method.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.1-7
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• 2020

The demand on the underwater communications is extremely increasing in searching for underwater resources, marine expedition, or environmental researches, yet there are many problems with the wireless communications because of the characteristics of the underwater environments. Especially, with the underwater wireless networks, there happen inevitable delay time and spacial inequality due to the distances between the nodes. To solve these problems, this paper suggests a new solution based on ALOHA-Q. The suggested method use random NAV value. and Environments take reward through communications success or fail. After then, The environments setting NAV value from reward. This model minimizes usage of energy and computing resources under the underwater wireless networks, and learns and setting NAV values through intense learning. The results of the simulations show that NAV values can be environmentally adopted and select best value to the circumstances, so the problems which are unnecessary delay times and spacial inequality can be solved. Result of simulations, NAV time decreasing 17.5% compared with original NAV.

• International Journal of Fluid Machinery and Systems
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• v.2 no.4
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• pp.286-294
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• 2009

Hydroelectric power plants are known for their ability to cover variations of the consumption in electrical power networks. In order to follow this changing demand, hydraulic machines are subject to off-design operation. In that case, the swirling flow leaving the runner of a Francis turbine may act under given conditions as an excitation source for the whole hydraulic system. In high load operating conditions, vortex rope behaves as an internal energy source which leads to the self excitation of the system. The aim of this paper is to identify the influence of the full load excitation source location with respect to the eigenmodes shapes on the system stability. For this, a new eigenanalysis tool, based on eigenvalues and eigenvectors computation of the nonlinear set of differential equations in SIMSEN, has been developed. First the modal analysis method and linearization of the set of the nonlinear differential equations are fully described. Then, nonlinear hydro-acoustic models of hydraulic components based on electrical equivalent schemes are presented and linearized. Finally, a hydro-acoustic SIMSEN model of a simple hydraulic power plant, is used to apply the modal analysis and to show the influence of the turbine location on system stability. Through this case study, it brings out that modeling of the pipe viscoelastic damping is decisive to find out stability limits and unstable eigenfrequencies.