• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.327-330
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• 2003

This paper proposes an estimation algorithm for the generating power of distributed generations(DG) interconnected with distribution networks. These days, DG are rapidly increasing and most of them are interconnected with distribution networks. The DG can supply power into the distribution network, which may make significant impact on fault current and the protection scheme of the interconnected distribution networks. Generally these influences of DG is proportioned as the distributed generator's power. Therefore, it is important to forecast the output power of distributed generator in PCC(point of common coupling). This paper presents the prediction method of DG's power by monitoring the current and phase difference.

• East Asian Economic Review
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• v.24 no.2
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• pp.165-184
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• 2020

Applying Ismailov and Rossi (2018), I newly construct the Korea FX uncertainty based on the density distribution of historical forecast errors. This uncertainty index properly captures the rare but significant events in the Korean currency market and provides information distinct from other uncertainty measures in recent studies. I show that 1) FX uncertainty arising from unexpected depreciation has a stronger impact on Korea-U.S. exchange rates and that 2) macro variables, such as capital flows or interest rate differentials, have predictive ability regarding Korea FX uncertainty for short horizons. These findings enable us to predict the events of sudden currency crashes and understand the Korea-U.S. exchange rate dynamics.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.194-194
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• 2017

Flooding is one of the most serious and frequently occurred natural disaster at many regions around the world. Especially, under the climate change impact, it is more and more increasingly trend. To reduce the flood damage, flood forecast and its accuracy analysis are required. This study is conducted to analyze the accuracy of the real-time flood forecasting of a coupled meteo-hydrological model for the Han River basin, South Korea. The LDAPS (Local Data Assimilation and Prediction System) products with the spatial resolution of 1.5km and lead time of 36 hours are extracted and used as inputs for the SURR (Sejong University Rainfall-Runoff) model. Three statistical criteria consisting of CC (Corelation Coefficient), RMSE (Root Mean Square Error) and ME (Model Efficiency) are used to evaluate the performance of this couple. The results are expected that the accuracy of the flood forecasting reduces following the increase of lead time corresponding to the accuracy reduction of LDAPS rainfall. Further study is planed to improve the accuracy of the real-time flood forecasting.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.233-250
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• 2021

This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition

• KDI Journal of Economic Policy
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• v.44 no.3
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• pp.49-74
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• 2022

This paper empirically examines how fiscal instability affects financial instability. According to an IMF forecast (2021a), the fiscal space in Korea will be steadily reduced in the future. The theoretical literature predicts that if fiscal stability is undermined, financial stability will also be in danger given that government guarantees on banks are weakened and/or sovereign bonds held in banks become riskier. This paper empirically finds the existence of this negative impact of fiscal instability on financial instability. I also find that the intensity of this fiscal-financial relationship is greater in a country where (i) its currency is not a reserve currency such as the US dollar or euro, (ii) its banking sector is large relative to government sector, and/or (iii) its private credit to GDP is high. Korea has all of these three characteristics and hence needs to put more effort into maintaining fiscal stability.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.72.2-72.2
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• 2021

Cosmic reionization imprints its history on the sky map of the cosmic microwave background (CMB) polarization. Even though mild, the signature of the reionization history during its early phase (z>15) can also impact the CMB polarization. We forecast the observational capability of the LiteBIRD(Lite(Light) satellite for the studies of B-mode polarization and Inflation from cosmic background Radiation Detection), a truly cosmic-variance limited apparatus. We focus on the capability for such an apparatus to probe the partial optical depth of the CMB photons during z>15. We show that LiteBIRD is able to probe this quantity with a modest to high significance, enabling one to tell how efficient the cosmic reionization and star formation were at z>15.

• KDI Journal of Economic Policy
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• v.28 no.2
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• pp.1-49
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• 2006

Considering the fact that households' demographic characteristics affect consumption decision, it is conjectured that rapid demographic changes would lead to a substantial change in the composition of private consumption expenditure. This paper estimates the demand functions of various consumption items by applying the Quadratic Almost Ideal Demand System(QUAIDS) model to Household Income and Expenditure Survey data, and then provides a long-term forecast of the composition of household consumption expenditure for 2005-2020. The paper shows that Korea's consumption expenditure will maintain the recent years' rapid change, of which a considerable portion is due to rapid demographic changes. Results of the paper can be utilized in forecasting the change in the industrial structure of the economy, as well as in firms' investment planning.

• Atmosphere
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• v.24 no.3
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• pp.343-364
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• 2014

This study presents local and synoptic conditions associated with extreme heavy snowfall events in the Yeongdong region, as well as the temporal and spatial variability of these conditions. During the last 12 years (2001~2012), 3 extreme snowfall events occurred in the Yeongdong region, which recorded daily snowfall greater than 50 cm, respectively. In these events, one of the noticeable features is the occurrence of heavy hourly snowfall greater than 10 cm. It was reported from satellite analysis that these heavy snowfall may be closely related to mesoscale convective clouds. In this paper the 3 extreme events are examined on their synoptic environments associated with the developments of mesoscale convective system using numerical model output. These 3 events all occurred in strongly forced synoptic environments where 500 and 300 hPa troughs and 500 hPa thermal troughs were evident. From the analysis of diagnostic variables, it was found in all 3 events that absolute vorticity and cold air advection were dominant in the Yeongdong region and its surrounding sea at upper levels, especially at around 500 hPa (absolute vorticity: $20{\sim}60{\times}10^{-5}s^{-1}$, cold air advection: $-10{\sim}-20^{\circ}C$ $12hr^{-1}$). Moreover, the spatial distributions of cold advection showed mostly the shape of a narrow band along the eastern coast of Korea. These features of absolute vorticity and cold advection at 500 hPa were sustained for about 10 hours before the occurrence of maximum hourly snowfall.

• Atmosphere
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• v.30 no.2
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• pp.155-167
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• 2020

In order to produce more detailed and accurate information of river discharge and freshwater discharge, global high-resolution hydrodynamic model (CaMa-Flood) is applied to an operational land surface model of global seasonal forecast system. In addition, bias correction to grid runoff for the hydrodynamic model is attempted. CaMa-Flood is a river routing model that distributes runoff forcing from a land surface model to oceans or inland seas along continentalscale rivers, which can represent flood stage and river discharge explicitly. The runoff data generated by the land surface model are bias-corrected by using composite runoff data from UNH-GRDC. The impact of bias-correction on the runoff, which is spatially resolved on 0.5° grid, has been evaluated for 1991~2010. It is shown that bias-correction increases runoff by 30% on average over all continents, which is closer to UNH-GRDC. Two experiments with coupled CaMa-Flood are carried out to produce river discharge: one using this bias correction and the other not using. It is found that the experiment adapting bias correction exhibits significant increase of both river discharge over major rivers around the world and continental freshwater discharge into oceans (40% globally), which is closer to GRDC. These preliminary results indicate that the application of CaMa-Flood as well as bias-corrected runoff to the operational global seasonal forecast system is feasible to attain information of surface water cycle from a coupled suite of atmospheric, land surface, and hydrodynamic model.

• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.