• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.1
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• pp.91-100
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• 2004

Energy efficiency evaluation method for a multi-type air conditioning system still has not been developed. In this study, analysis on capacity range and evaluating method of standards for air conditioners was conducted with world-wide Standards. It is not a proper approach to use the standards for residential air conditioner to multi type air conditioners. Some difficulties and problems are commented in this study with overview of the standards. Through the analytic research, an evaluating method for multi type air conditioner was suggested with Integrated Part Load Value (IPLV). The suggested concept for evaluating energy efficiency during part load condition considers building load pattern and operating hours of the system at different locations. Load was weighted in IPLV to consider not only the concept of occurrence of outdoor temperature such as bin method but also operation hours of the system. An experiment about the IPLV was conducted with variable air volume ducted type air conditioning system and multi-type system through modified code tester to give a glance at quantitative value of the IPLV.

• Atmosphere
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• v.28 no.3
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• pp.273-289
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• 2018

This paper evaluates daily precipitation products from Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission Multisatellite (TRMM) Precipitation Analysis (TMPA), and the Climate Prediction Center Morphing Method (CMORPH), validated against gauge observation over South Korea and gauge-based analysis data East Asia during one year from June 2014 to May 2015. It is found that the three products effectively capture the seasonal variation of mean precipitation with relatively good correlation from spring to fall. Among them, IMERG and TMPA show quite similar precipitation characteristics but overall underestimation is found from all precipitation products during winter compared with observation. IMERG shows reliably high performance in precipitation for all seasons, showing the most unbiased and accurate precipitation estimation. However, it is also noticed that IMERG reveals overestimated precipitation for heavier precipitation thresholds. This assessment work suggests the validity of the IMERG product for not only seasonal precipitation but also daily precipitation, which has the potential to be used as reference precipitation data.

• Journal of the Korean Solar Energy Society
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• v.40 no.5
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• pp.47-58
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• 2020

Ministry of Land, Infrastructure and Transport(MOLIT) has increased reduction rate from 18.1% to 32.7% in Building sector compared to BAU of the national greenhouse gas emission according to the 2030 Greenhouse Gas Reduction Road map Amendment. For this purpose, MOLIT has been activating the green remodeling projects for existing buildings. Considering that 15 year old buildings after completion are 74% (5.25 million buildings) among about 7 million existing building stocks in Korea, reduction of building energy consumption by green remodeling is urgently needed, However, it is a major difficulty of activation for green remodeling projects because there are few case studies on Before and After building energy consumption of actual green remodeling projects. Considering that building energy performance and value increase after green remodeling through previous researches, additional studies of the energy consumption assessment on actual green remodeling projects are essential. Therefore, this study aims to propose results on Before and After building energy consumption of actual green remodeling projects.

• The KIPS Transactions:PartC
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• v.9C no.6
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• pp.841-846
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• 2002

We applies a seasonal ARIMA model to the timely forecasting in a line utilization and its confidence interval on the base of the past data of the line utilization that QoS of the network is greatly influenced by. And this paper proposes the learning algorithm of dynamic threshold in line utilization using the SARIMA model. We can find the proper dynamic threshold in timely line utilization on the various network environments and provide the confidence based on probability. Also, we have evaluated the validity of the proposed model and estimated the value of a proper threshold on real network. Network manager can overcome a shortcoming of original threshold method and maximize the performance of this algorithm.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.118-118
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• 2020

This study investigates the change of flow characteristics over 10 Asian river basins in the past 30 years (1976-2005). The variation is estimated from The Soil and Water Assessment Tool (SWAT) model outputs based on reanalysis data which was bias-corrected for Asian monsoon reagion. The model was firstly calibrated and validated using observed data for daily streamflow. Four statistical criteria were applied to evaluate the model performance, including Coefficient of determination (R2), Nash - Sutcliffe model efficiency coeffi cient (NSE), Root mean square error-observations standard deviation ratio (RSR), and Percentage Bias (PBIAS). Then parameters of the model were applied for the historical period 1976-2005. The estimates show a temporal non-considerable increasing rate of daily streamflow in most of the basins over the past 30 years. The difference of monthly discharge becomes more significant during the months in the wet season (June to September) in all basins. The seasonal runoff shows significant difference in Summer and Autumn, when the rainfall intensity is higher. The line showing averaged runoff/rainfall ratio in all basins is sharp, presenting high variation of seasonal runoff/rainfall ratio from season to season.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• Atmosphere
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• v.19 no.3
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• pp.269-287
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• 2009

CAT (clear-air turbulence) forecasting algorithm, the Graphical Turbulence Guidance (GTG) system developed at NCAR (national center for atmospheric research), is evaluated with available observations (e.g., pilot reports; PIREPs) reported in South Korea during the recent 5 years (2003-2008, excluding 2005). The GTG system includes several steps. First, 44 CAT indices are calculated in the domain of the Regional Data Assimilation and Prediction System (RDAPS) analysis data with 30 km horizontal grid spacing provided by KMA (Korean Meteorological Administration). Second, 10 indices that performed ten best forecasting scores are selected. Finally, 10 indices are combined by measuring the score based on the probability of detection, which is calculated using PIREPs exclusively of moderate-or-greater intensity. In order to investigate the best performance of the GTG system in Korea, various statistical examinations and sensitivity tests of the GTG system are performed by yearly and seasonally classified PIREPs. Performances of the GTG system based on yearly distributed PIREPs have annual variations because the compositions of indices are different from each year. Seasonal forecasting is generally better than yearly forecasting, because selected CAT indices in each season represent meteorological condition much more properly than applying the selected CAT indices to all seasons. Wintertime forecasting is the best among the four seasonal forecastings. This is likely due to that the GTG system consists of many CAT indices related to the jet stream, and turbulence associated with the jet stream can be activated mostly in wintertime under strong jet magnitude. On the other hand, summertime forecasting skill is much less than other seasons. Compared with current operational CAT prediction system (KITFA; Korean Integrated Turbulence Forecasting System), overall performance of the GTG system is better when CAT indices are selected seasonally.

• Atmosphere
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• v.26 no.4
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• pp.599-615
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• 2016

This study investigates the accuracy of short-term ocean predictions during the development of ocean stratification for the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 5 (GloSea5) as well as the effect of atmosphere-ocean coupling on the predictions through a series of sensitive numerical experiments. Model performance is evaluated using the marine meteorological buoys at seas around the Korean peninsular (KP), Tropical Atmosphere Ocean project (TAO) buoys over the tropical Pacific ocean, and ARGO floats data over the western North Pacific for boreal winter (February) and spring (May). Sensitive experiments are conducted using an ocean-atmosphere coupled model (i.e., GloSea5) and an uncoupled ocean model (Nucleus for European Modelling of the Ocean, NEMO) and their results are compared. The verification results revealed an overall good performance for the SST predictions over the tropical Pacific ocean and near the Korean marginal seas, in which the Root Mean Square Errors (RMSE) were $0.31{\sim}0.45^{\circ}C$ and $0.74{\sim}1.11^{\circ}C$ respectively, except oceanic front regions with large spatial and temporal SST variations (the maximum error reached up to $3^{\circ}C$). The sensitive numerical experiments showed that GloSea5 outperformed NEMO over the tropical Pacific in terms of bias and RMSE analysis, while NEMO outperformed GloSea5 near the KP regions. These results suggest that the atmosphere-ocean coupling substantially influences the short-term ocean forecast over the tropical Pacific, while other factors such as atmospheric forcing and the accuracy of simulated local current are more important than the coupling effect for the KP regions being far from tropics during the development of ocean stratification.

• Atmosphere
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• v.28 no.1
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• pp.85-97
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• 2018

This study evaluates the performance of the Weather Research and Forecasting (WRF) model in reproducing the present-day (1981~2005) precipitation over Far East Asia and South Korea. The WRF model is configured with 25-km horizontal resolution within the context of the COordinated Regional climate Downscaling Experiment (CORDEX) - East Asia Phase 2. The initial and lateral boundary forcing for the WRF simulation are derived from European Centre for Medium-Range Weather Forecast Interim reanalysis. According to our results, WRF model shows a reasonable performance to reproduce the features of precipitation, such as seasonal climatology, annual and inter-annual variabilities, seasonal march of monsoon rainfall and extreme precipitation. In spite of such model's ability to simulate major features of precipitation, systematic biases are found in the downscaled simulation in some sub-regions and seasons. In particular, the WRF model systematically tends to overestimate (underestimate) precipitation over Far East Asia (South Korea), and relatively large biases are evident during the summer season. In terms of inter-annual variability, WRF shows an overall smaller (larger) standard deviation in the Far East Asia (South Korea) compared to observation. In addition, WRF overestimates the frequency and amount of weak precipitation, but underestimates those of heavy precipitation. Also, the number of wet days, the precipitation intensity above the 95 percentile, and consecutive wet days (consecutive dry days) are overestimated (underestimated) over eastern (western) part of South Korea. The results of this study can be used as reference data when providing information about projections of fine-scale climate change over East Asia.