• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• Environmental and Resource Economics Review
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• v.29 no.4
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• pp.419-442
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• 2020

The majority of the natural gas demand in South Korea is mainly determined by the heating demand. Accordingly, there is a distinct seasonality in which the gas demand increases in winter and decreases in summer. Moreover, the degree of sensitiveness to temperature on gas demand has changed over time. This study firstly introduces changing temperature response function (TRF) to capture effects of changing seasonality. The temperature effect (TE), estimated by integrating temperature response function with daily temperature density, represents for the amount of gas demand change due to variation of temperature distribution. Also, this study presents an innovative way in forecasting daily temperature density by employing functional principal component analysis based on daily max/min temperature forecasts for the five big cities in Korea. The forecast errors of the temperature density and gas demand are decreased by 50% and 80% respectively if we use the proposed forecasted density rather than the average daily temperature density.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6860-6868
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• 2015

In this study, we consider a problem of forecasting daily city gas demand of Korea. Forecasting daily gas demand is a daily routine for gas provider, and gas demand needs to be forecasted accurately in order to guarantee secure gas supply. In this study, we analyze the time series of city gas demand in several ways. Data analysis shows that primary factors affecting the city gas demand include the demand of previous day, temperature, day of week, and so on. Incorporating these factors, we developed a multiple linear regression model. Also, we devised a sampling procedure that selectively collects the past data considering the characteristics of the city gas demand. Test results on real data exhibit that the MAPE (Mean Absolute Percentage Error) obtained by the proposed method is about 2.22%, which amounts to 7% of the relative improvement ratio when compared with the existing method in the literature.

• Bulletin of the Korea Photovoltaic Society
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• v.4 no.2
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• pp.45-53
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• 2018

Natural gas demand for power generation continued to increase until 2013 due to the expansion of large-scale LNG power plants after the black-out of 2011. However, natural gas demand for power generation has decreased sharply due to the increase of nuclear power and coal power generation. But demand for power generation has increased again as energy policies have changed, such as reducing nuclear power and coal power plants, and abnormal high temperatures and cold waves have occurred. If the gas pipeline pressure can be properly maintained by predicting these fluctuations, it can contribute to enhancement of operation efficiency by minimizing the operation time of facilities required for production and supply. In this study, we have developed a regression model with daily power demand and base power generation capacity as explanatory variables considering characteristics by day of week. The model was constructed using data from January 2013 to December 2016, and it was confirmed that the error rate was 4.12% and the error rate in the 90th percentile was below 8.85%.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.105-108
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• 2007

Fuel cell with high electric efficiency has many probabilities of commercial use. Especially, proton exchange membrane fuel cell (PEMFC) which is a low temperature fuel cell and has less influence on $CO_2$ concentration is considered the power generation system of small building and household. We calculated the optimal operational plans of 1 kW household PEMFC power system based on the daily electricity and heat demand patterns of Japan and Korea. Calculated results show that the economic feasibility of PEMFC power system is very sensitive to the cost policy of electricity and natural gas.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.31-34
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• 2007

Fuel cell with high electric efficiency has many probabilities of commercial use. Especially, polymer electrolyte or proton exchange membrane fuel cell (PEMFC) which is a low operating temperature and has less influence on $CO_2$ concentration is considered the power generation system of small building and household. We calculated the optimal operational plans of 1 kW household PEMFC power system based on daily electric and heat demand patterns of various size of apartments. Calculated results show that the economic feasibility of PEMFC power system is very sensitive to the cost policy of electricity and natural gas.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.3
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• pp.315-319
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• 2020

The modern transportation and mobility sector is expected to encounter high penetration of Electric Vehicles (EVs) because EVs contribute to reducing the harmful emissions from fossil fuel-powered vehicles. With the prospective growth of EVs, sufficient and convenient facilities for fast charging are crucial toward satisfying the EVs' quick charging demand during their trip. Therefore, the Fast Electric Vehicle Charging Stations (FECS) will be a similar role to gas stations. In this paper, we study a charging scheduling problem for the FECS with solar photovoltaic (PV) and an Energy Storage System (ESS). We formulate an optimization problem that minimizes the operational costs of FECS. There are two cost and one revenue terms that are buying cost from main grid power, ESS degradation cost, and revenue from the charging fee of the EVs. Simulation results show that the proposed scheduling algorithm reduces the daily operational cost by effectively using solar PV and ESS.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.125-140
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• 2018

This study uses daily price data on three major types of crude oil (Brent, Dubai, and WTI) and BDI from January 2, 2009 to June 29, 2018, to compare the relationship between crude oil prices and BDI for rate of change and volatility. Unlike previous studies, the correlation between BDI and crude oil prices was analyzed both the rate of change and variability, VARs, Granger Causality Test, and the GARCH and DCC models were employed. The correlation analysis, indicated that the crude oil price change rate and volatility affect the BDI change rate and that BDI volatility affects the crude oil price change rate and volatility. The relationship between oil prices and BDI is identified, but their correlation is low, which is likely a result of lower dependence on crude oil as demand for natural gas increases worldwide and demand for renewable energy decreases. These trends could result in lower correlations over time. Therefore, focusing on the changing demand for raw materials in future investments in international shipping(real economy) and oil markets and macroeconomic analysis is necessary.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.1-7
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• 2019

In response to the recent increase in demand for hydrogen stations, the Ministry of Trade and Industry has enacted and promulgated special notifications to enable the installation of hydrogen stations in the form of the combined complex in existing automotive fuel supply facilities such as LPG, CNG, and gas stations. Hydrogen multi energy filling stations haven't been operated yet in Korea till the establishment of special standards, so it is necessary create special standards by considering all Korean environmental characteristics such as four seasons and daily crossings. In this study, we collected and analyzed the charging data of Ulsan LPG-Hydrogen Multi Fueling Station installed for the first time in Korea. The data are hourly temperature and pressure data from compressors, storage vessels and dispensers. We used the data collected for a year, including the highest temperature and the lowest temperature in Ulsan to compare seasonal characteristics. As a result, it was found that the change of the outside temperature affects the initial temperature of the vehicle's container of the hydrogen car, which finally affects the charging time and the charging speed of the vehicle. There was no effect on vehicle containers because the limit temperature suggested by the Korean Hydrogen Station Standard(KGS FP217) and the US Filling Protocol(SAE J2601) was not exceeded.