• Journal of Energy Engineering
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• v.20 no.4
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• pp.318-329
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• 2011

This study focuses on enhancing the accuracy of consumption function of Korean natural gas for city gas. It is using time-series model with time-varying coefficients taking into account the recent abnormal temperature phenomenon and the changing gross domestic product (GDP) as important variables. This study estimates the cointegrating regression model for the long-run estimation and the error correction model for the short-run estimation. The consumption function of Korean natural gas is estimated to be influenced by the time-varying coefficients of GDP and temperature. Using the estimated time-series model with time-varying coefficients, this study forecasts the consumption of natural gas for city gas from July 2011 to December 2012. The consumption in 2011 would be 18,303 thousand tons, which is little different from the imported 18,681 thousand tons. The consumption of natural gas for city gas in 2012 is forecast to be 19,213 thousand tons. The consumption model of this study is needed to extend by considering the relative prices between natural gas and its substitutes, the scale of consumers and others.

• Journal of Energy Engineering
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• v.23 no.3
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• pp.36-41
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• 2014

The world's overall need for natural gas (NG) has been growing up fast, especially in the residential sector. The better the estimation of residential NG consumption (RNGC) distribution, the better decision-making for a residential NG policy such as pricing, demand estimation, management options and so on. Approximating the distribution of RNGC is complicated by zero observations in the sample. To deal with the zero observations by allowing a point mass at zero, a mixture model of RNGC distributions is proposed and applied. The RNGC distribution is specified as a mixture of two distributions, one with a point mass at zero and the other with full support on the positive half of the real line. The model is empirically verified for household RNGC survey data collected in Korea. The mixture model can easily capture the common bimodality feature of the RNGC distribution. In addition, when covariates were added to the model, it was found that the probability that a household has non-expenditure significantly varies with some variables. Finally, the goodness-of-fit test suggests that the data are well represented by the mixture model.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.383-384
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• 2022

Gas Combustion Unit (GCU) onboard liquefied natural gas carriers handles boil-off to stabilize tank pressure. There are many factors for LNG cargo operators to take into consideration to determine whether to use GCU or not. Gas consumption of main engine and re-liquefied gas through the Partial Re-Liquefaction System (PRS) are good examples of these factors. Human gas operators have decided the operation so far. In this paper, some deep learning neural network models were developed to provide human gas operators with a decision support system. The models consider various factors specially into GCU operation. A deep learning model with Sigmoid activation functions in input layer and hidden layers made the best performance among eight different deep learning models.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.29-36
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• 2016

In this study, diesel/natural gas dual-fuel engine was studied numerically using DoE method. The engine is CI engine for power generation and modelled by 1-D simulation GT-power. The combustion and emission characteristics were analyzed as a function of IVO, IVC and the ratio of natural gas to total fuel enegy. As the proportion of natural gas increases, the BSFC(Brake specific fuel consumption) is increased and BSNOx(Brake specific NOx) is decreased. If specific valve timing to improve the BSFC is applied to the engine, the BSFC is decreased by 1% and simultaneously BSNOx is decreased by 36%.