• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.719-724
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• 2006

The purpose of this study is on the performance evaluation of horizontally installed HGHEX(Horizontal-type Geothermal Heat Exchanger) in the summer season and the winter season. Followings are the results. By the result of data acquisition at the site, $2.5{\sim}2.7^{\circ}C$ temperature differences are gained between supply pipes and return pipes of HGHEX in the summer season. And $0.5{\sim}1.5^{\circ}C$ temperature differences are gained from HGHEX in the winter season. With these temperature differences, heat quantity of rejection and absorption is calculated and the performance of HGHEX is evaluated according to the seasons.

• Atmosphere
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• v.28 no.1
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• pp.15-24
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• 2018

Sea ice plays an important role in modulating surface conditions at high and mid-latitudes. It reacts rapidly to climate change, therefore, it is a good indicator for capturing these changes from the Arctic climate. While many models have been used to study the predictability of climate variables, their performance in predicting sea ice was not well assessed. This study examines the predictability of the Arctic sea ice extent from ensemble prediction systems. The analysis is focused on verification of predictability in each model compared to the observation and prediction in particular, on lead time in Sub-seasonal to Seasonal (S2S) scales. The S2S database now provides quasi-real time ensemble forecasts and hindcasts up to about 60 days from 11 centers: BoM, CMA, ECCC, ECMWF, HMCR, ISAC-CNR, JMA, KMA, Meteo France, NCEP and UKMO. For multi model comparison, only models coupled with sea ice model were selected. Predictability is quantified by the climatology, bias, trends and correlation skill score computed from hindcasts over the period 1999 to 2009. Most of models are able to reproduce characteristics of the sea ice, but they have bias with seasonal dependence and lead time. All models show decreasing sea ice extent trends with a maximum magnitude in warm season. The Arctic sea ice extent can be skillfully predicted up 6 weeks ahead in S2S scales. But trend-independent skill is small and statistically significant for lead time over 6 weeks only in summer.

• Journal of the Korean Wood Science and Technology
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• v.51 no.4
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• pp.295-308
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• 2023

This study applied a real-time IoT (Internet of Things) environmental monitoring system to wooden cultural heritages (WCHs) located in suburbs and forests in Korea. It automated the graphs of seasonal Temperature (T) and relative humidity (RH) changes inside the heritage structures and seasonal Performance Index (PI) values. While utilizing line graphs of the existing T and RH change trends and a bar graph expressing the PI values, this study examined the current status of the conservation environment inside the WCHs throughout the year and its diagnosis. Consequently, at higher latitudes, the organic cultural heritage repeatedly experienced large T fluctuations, and the risk of physical and chemical degradation of the materials was greater. However, the RH showed significant seasonal differences, even within the same latitude, indicating that the impact of latitude was not significant. Therefore, the staff in charge must manage RH by considering the surrounding environmental conditions and adjusting the internal environment of the structures. The PI values for the year-round T and RH inside the heritages were confirmed to only be a maximum of approximately 60% of the environmental suitability for conservation throughout the year, depending on the season. The relationship between the germination and growth potential period of xerophilic fungi and the monthly internal temperature and humidity in five heritages located at different latitudes was analyzed. As a result, we could thus determine that four particular months of the year (June-September) represent the periods with the highest risk of damage from xerophilic fungi in the country, regardless of latitude.

• Proceedings of the SAREK Conference
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• 2007.06a
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• pp.82-82
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• 2007

• Proceedings of the SAREK Conference
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• 2005.06a
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• pp.42-42
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• 2005

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.6
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• pp.392-399
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• 2011

Ground-coupled heat pump(GCHP) systems have been shown to be an environmentally-friendly, efficient alternative to traditional cooling and heating systems in both residential and commercial applications. Although some work related to performance evaluation of GCHP systems for commercial buildings has been done, relatively little has been reported on the residential applications. The aim of this study is to evaluate the cooling and heating performances of a vertical GCHP system applied to an artificial detached house($117\;m^2$) in Seoul. For this purpose, a typical design procedure was involved with a combination of design parameters such as building loads, heat pump capacity, borehole diameter, and ground thermal properties, etc. The cooling and heating performance simulation of the system was conducted with different prediction times of 8760 hours and 240 months. The performance characteristics including seasonal system COP, average annual power consumption, and temperature variations related to ground heat exchanger were calculated and compared.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.4
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• pp.37-46
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• 1999

Field experimnet of constructed wetland for rural wastewater treatment was performed from July 1998 to April 1999 including winter to examine the seasonal effect on the wetland performance. The system worked without freezing even under $-10^{\circ}C$ of air temperature as long as watewater was flowing. BOD removal rates varied in similar pattern as the air temperature, and winter performance was relatively lower than that in the growing season. However, removing performance during winter was still significant, and BOD removal rates were almost the the same as in the growing season. SS removal rate was relativelyless affected by temmperature, but lower decay rate during the winter can result in accumulation of the SS in the system, which releases constituents in the next spring and can affect whole system performance. The winter removal rates of nutrients like T-N and T-P were decreased about half compared to the growing season and low temperature. To maintain stabilized wetland performanced including winter time, supplying minimum heating for plants could be an alternative in field application. Experimental data was compared with NADB(North Americal Wetlands for Water Quality treatment database), and general performance of the system was within the reasonable range. The pollutant loading and effluent concentration of the experimented system were in high margin. Base on the experiment and databases, the required effluent water quality could be achieved if loading rate adjusted as ilulstrated in the database.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.507-516
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• 2012

We study time series models for seasonal time series data with a covariance structure that depends on time and the periodic autocorrelation at various lags $k$. In this paper, we introduce an ARMA model with periodically varying coefficients(PARMA) and analyze Arosa ozone data with a periodic correlation in the practical case study. Finally, we use a PARMA model and a seasonal ARIMA model for data analysis and show the performance of a PARMA model with a comparison to the SARIMA model.

• Water for future
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• v.28 no.1
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• pp.81-90
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• 1995

This study attempts to develop a stochastic system model for extension and prediction of monthly runoff series in river basins where the observed runoff data are insufficient although there are long-term hydrometeorological records. For this purpose, univariate models of a seasonal ARIMA type are derived from the time series analysis of monthly runoff, monthly precipitation and monthly evaporation data with trend and periodicity. Also, a causual model of multiple input-single output relationship that take monthly precipitation and monthly evaporation as input variables-monthly runoff as output variable is built by the cross-correlation analysis of each series. The performance of the univariate model and the multiple input-output model were examined through comparisons between the historical and the generated monthly runoff series. The results reveals that the multiple input-output model leads to the improved accuracy and wide range of applicability when extension and prediction of monthly runoff series is required.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.275-284
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• 2016

Accurate electricity demand forecasts is essential in reducing energy spend and preventing imbalance of the power supply. In forcasting electricity demand, we considered double seasonal Holt-Winters model and TBATS model with sliding window. We selected a specific time zone as the reference line of daily electric demand because it is least likely to be influenced by external factors. The forecasting performance have been evaluated in terms of RMSE and MAPE criteria. We used the observations ranging January 4, 2009 to December 31 for testing data. For validation data, the records has been used between January 1, 2012 and December 29, 2012.