• Journal of Energy Engineering
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• v.22 no.4
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• pp.370-375
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• 2013

This paper attempts to estimate the city gas demand function in Korea over the period 1981-2012. As the city gas demand function provides us information on the pattern of consumer's city gas consumption, it can be usefully utilized in predicting the impact of policy variables such as city gas price and forecasting the demand for city gas. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the city gas demand function. The results show that short-run price and income elasticities of the city gas demand are estimated to be -0.522 and 0.874, respectively. They are statistically significant at the 1% level. The short-run price and income elasticities portray that demand for city gas is price- and income-inelastic. This implies that the city gas is indispensable goods to human-being's life, thus the city gas demand would not be promptly adjusted to responding to price and/or income change. However, long-run price and income elasticities reveal that the demand for city gas is price- and income-elastic in the long-run.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1275-1288
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• 2015

A microscale weather analysis module (about 1km or less) is a microscale numerical weather prediction model designed for operational forecasting and atmospheric research needs such as radiant energy, thermal energy, and humidity. The accuracy of the module is directly related to the usefulness and quality of real-time microscale weather information service in the metropolitan area. This paper suggests an object based verification method useful for spatio-temporal evaluation of the accuracy of the microscale weather analysis module. The method is a graphical method comprised of three steps that constructs a lattice field of evaluation statistics, merges and identifies objects, and evaluates the accuracy of the module. We develop lattice fields using various evaluation spatio-temporal statistics as well as an efficient object identification algorithm that conducts convolution, masking, and merging operations to the lattice fields. A real data application demonstrates the utility of the verification method.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.2
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• pp.122-130
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• 2014

Seasonal variations and long term linear trends of SST (Sea Surface Temperature) at Yeosu Coast ($127^{\circ}37.73^{\prime}E$, $34^{\circ}37.60^{\prime}N$) in Korea were studied performing the harmonic analysis and the regression analysis of the monthly mean SST data of 46 years (1965-2010) collected by the Fisheries Research and Development Institute in Korea. The mean SST and the amplitude of annual SST variation show $15.6^{\circ}C$ and $9.0^{\circ}C$ respectively. The phase of annual SST variation is $236^{\circ}$. The maximum SST at Yeosu Coast occurs around August 26. Climatic changes in annual mean SST have had significant increasing tendency with increase rate $0.0305^{\circ}C/Year$. The warming trend in recent 30 years (1981-2010) is more pronounced than that in the last 30 years (1966-1995) and the increasing tendency of winter SST dominates that of the annual SST. The time series model that could be used to forecast the SST on a monthly basis was developed applying Box-Jenkins methodology. $ARIMA(1,0,0)(2,1,0)_{12}$ was suggested for forecasting the monthly mean SST at Yeosu Coast in Korea. Mean absolute percentage error to measure the accuracy of forecasted values was 8.3%.

• Journal of Energy Engineering
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• v.21 no.1
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• pp.33-42
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• 2012

In this study, long-term greenhouse gas reductions expected passenger sector was used for the MESSAGE. Green Car road map proposed BAU scenario, Enhanced diffusion green car scenario, and price 1, 2 scenarios was configured with four scenarios. Enhanced diffusion green car in the scenario, in 2050 compared to BAU scenario 13% of the emissions will decrease. Price 1 and Price 2 scenario is emissions reduction of 14% compared to BAU. This study consists of six chapters. Introduction of MESSAGE, creation and RES in the year and the target year set a different base line and the passenger building materials sector activities, steps for passenger sector scenario and Based on the results of running the emissions reductions were to describe.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.36-52
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• 2020

This study performed 2-D(two-dimensional) hydrodynamic analysis using EFDC in the Nakdong River. For the simulation of the flood season and non-flood season, the measured data including water level, weir outflow and tributary inflow were used, and the accuracy and applicability of the model were verified by comparing the measured water level and computed one. In addition, statistical quantitative assessment of the model performance was performed by estimating PBIAS, RSR, and RMSE for the computed water level. Then, the average velocity for each section between weirs was calculated by applying constant discharge conditions, and it was compared and verified with the measured velocity by Hydrological Survey Center. In this study, a simple method for estimating the arrival time was proposed, and it is expected that it will be practically applicable in field practices such as flood forecasting and warning.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• Atmosphere
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• v.21 no.2
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• pp.163-172
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• 2011

Numerical forecasting depends on the initial condition error strongly because numerical model is a chaotic system. To calculate the sensitivity of some forecast aspects to the initial condition in the Korea Meteorological Administration (KMA) Unified Model (UM) which is originated from United Kingdom (UK) Meteorological Office (MO), an algorithm to calculate adjoint sensitivities is developed by modifying the adjoint perturbation forecast model in the KMA UM. Then the new algorithm is used to calculate adjoint sensitivity distributions for typhoon DIANMU (201004). Major initial adjoint sensitivities calculated for the 48 h forecast error are located horizontally in the rear right quadrant relative to the typhoon motion, which is related with the inflow regions of the environmental flow into the typhoon, similar to the sensitive structures in the previous studies. Because of the upward wave energy propagation, the major sensitivities at the initial time located in the low to mid- troposphere propagate upward to the upper troposphere where the maximum of the forecast error is located. The kinetic energy is dominant for both the initial adjoint sensitivity and forecast error of the typhoon DIANMU. The horizontal and vertical energy distributions of the adjoint sensitivity for the typhoon DIANMU are consistent with those for other typhoons using other models, indicating that the tools for calculating the adjoint sensitivity in the KMA UM is credible.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.182-189
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• 2017

Photovoltaic power generation systems make up a large part of the low carbon energy trend. The purpose of this study is to utilize PVsyst, a commercial forecasting program, to forecast research on the design stages of photovoltaic power generation for wider applications of this system in railroads and to consider prospective issues for photovoltaic power plants that are currently being operated. Given this, we will compare the forecast value of generated photovoltaic power, derived from foreign weather forecast information provided by NASA, along with information from Meteonorm, and the forecast values derived from the KMA weather information. By comparing these values with amounts actually generated by KPX, this research aims to secure propriety rights for wider application of photovoltaic power generation systems in railroads, and to contribute to low carbon energy for the new climate of the future.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.133-141
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• 2010

MG(Microgrid) is a small power supply system located on-site that can supply both the electricity and the hot-water simultaneously. Engineering S/W is requested to construct Microgrids economically. We developed Engineering S/W that can combine DERs (Distributed Energy Resources) most economically using the linear programming and estimate of the economic. Developed S/W was programed using GAMS(General Algebraic Modeling System) and it is composed of the optimal DER combination module and forecasting module of renewable energy's output. We embody it based on MS Excel considering the user's convenience and we show its validity through a case study. We think that developed S/W will be very useful for planning MGs and energy supply.