• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.97-100
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• 2007

There are regulations on each building for its classification and It is corresponding determined contract demand. For transformer's capability calculation algorithm, cumulated power information of each customer is used to analysis the correlation between power usage and Demand Rate. By modeling this using Least Square Method, it can be targeted to recognize the pattern of transformer use in the past and make a prediction on it in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1823-1831
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• 2011

When the RPS(Renewable Portfolio Standards) becomes effective in 2012, the use of renewable energy will be dramatically increased. However, there are no production simulations and demand supply programs that reflect the characteristics of the renewable energy. This paper analyzes correlations of the domestic wind power and solar power generation pattern in different areas and those of these sources' output and load pattern. Based on the regional correlation analysis, an appropriate method that uses a average output of the renewable energy or another modeling that takes account of uncertainty could be selected. Because it's output is dependent on weather condition, we can not control the generation of renewable energy, that is the reason why the correlation between the load and output pattern of sources can be helpful to determine whether the renewable energy is modeled as a generator or load modifier. Through this analysis, a basis will be provided in order to properly model the renewable energy source.

• Korean Journal of Community Nutrition
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• v.8 no.6
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• pp.910-918
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• 2003

The purposes of this study were to develop a model for university foodservices and to provide management strategies for reducing costs, and increasing productivity and customer satisfaction. The results of this study were as follows : 1) The demands in university food services varied depending on the time series. A fixed pattern was discovered for specific times of the month and semesters. The demand tended to constantly decrease from the beginning of a specific semester to the end, from March to June and from September to December. Moreover, the demand was higher during the first semester than the second semester, within school term than during vacation periods, and during the summer vacation than the winter. 2) Pearson's simple correlation was done between actual customer demand and the factors relating to forecasting the demand. There was a high level of correlation between the actual demand and the demand that had occurred in the previous weeks. 3) By applying the stepwise multiple linear regression analysis to two different university food services providing multiple menu items, a model was developed in terms of four different time series(first semester, second semester, summer vacation, and winter vacation). Customer preference for specific menu items was found to be the most important factor to be considered in forecasting the demand.

• KIEAE Journal
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• v.11 no.5
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• pp.91-96
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• 2011

This study is to analysis the characteristics of electric power consumption of apartments complex in Korea. This study shows the pattern of electric power consumption and correlation of each apartment complex's completion year monthly and timely. With this result, we are able to predict the demand pattern of electricity in a house and make the schedule by demand pattern. It is expected this data is used as reference of electric consumption of Daejeon area to operate the simulation tools to predict the building energy. The yearly data of 10 apartment complexes of 2010 are analyzed. The results of this study are followed. The averaged amount of electricity consumption in winter is higher as summer because of the high capacity of heating equipment. All of the house has electric base load from 0.26kWh to 0.5kWh. The average of the electricity consumption of month is shown as 310.2kWh. A week is seperated, as 4 part such as week, weekend, Saturday and Sunday. During week, the average of timely electricity consumption is shown as 0.426kWh. The Saturday consumption is 0.437kWh. The Sunday is 0.445kWh. The peak electricity consumption in summer and winter is measured. The peak consumption on summer season is 1.389kW on 22th August 64% higher than winter season 0.887kW on 3rd January.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.221-229
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• 2023

Extracted from the metropolitan area, the Paldang Lake, which supplies approximately 8 million tons of water, has achieved a BOD (Biochemical Oxygen Demand) of 1.1 mg/L as a result of water quality preservation policies. However, concerning the COD (Chemical Oxygen Demand) component that encompasses refractory organic matter, there has been an observable upward trend in concentration. The introduction of refractory organic matter into the water source of Paldang Lake brings potential increments in BOD, generates off-putting tastes and odors in tap water, increases THM (Trihalomethane) formation, and triggers algae proliferation. Moreover, if residual hazardous refractory pollutants persist in aquatic environments, they may induce endocrine disruption and phenomena such as antibiotic resistance. In this study, a monitoring campaign was executed to discern the concentration of refractory organic matter emissions from point and non-point sources within Paldang Lake and its upstream region, with the aim of managing refractory organic matter in Paldang Lake. By comparing refractory organic matter emission concentrations across monitored areas, the elimination efficiency at wastewater treatment plants was assessed. Additionally, employing the Pearson correlation correlation analysis technique, correlations among refractory organic matter indices, antecedent wet days, and antecedent dry days were explored. The concentrations of refractory organic matter in rivers and Paldang Lake exhibited a similar pattern. Wastewater treatment plant effluents exhibited higher concentrations compared to rivers and Paldang Lake. The assessment of refractory organic matter removal at wastewater treatment plants indicated a removal efficiency of 65.73%. However, no significant correlation emerged between refractory organic matter emission concentration and antecedent wet days or priory antecedent dry days. This absence of correlation is attributed to data scarcity, underscoring the need for long-term monitoring and data accumulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.1-10
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• 1994

The purpose of this study is to search for the most effective forecasting model for condenser with independent demand among the quantitative methods such as Brown's exponential smoothing method, Box-Jenkins method, and multiple regression analysis method. The criterion for the comparison of the above models is mean squared error(MSE). The fitting results of these three methods are as follows. 1) Brown's exponential smoothing method is the simplest one, which means the method is easy to understand compared to others. But the precision is inferior to other ones. 2) Box-Jenkins method requires much historic data and takes time to get to the final model, although the precision is superior to that of Brown's exponential smoothing method. 3) Regression method explains the correlation between parts with similiar demand pattern, and the precision is the best out of three methods. Therefore, it is suggested that the multiple regression method is fairly good in precision for forecasting our item and that the method is easily applicable to practice.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.603-614
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• 2008

End-uses of household water have been changed by a life style, housing type, weather, water rate and water supply facilities etc. and those variables can be considered as an internal and exogenous factors to estimate long-term demand forecasts. Analysis of influential factors on water consumption in households would give an explanation to cause on the change of trend and would help predicting the water demand of end-use in household. The purpose of this study is to analyze the demand trends and patterns of household water uses by metering and questionnaire such as occupation, revenue, numbers of family member, housing types, age, floor area and installation of water saving device, etc. The peak water uses were shown at Saturday among weekdays and July in a year based on the analysis results of water use pattern. A steep increase of total water volume can be found in the analysis of water demand trend according to temperature from $-14^{\circ}C$ to $0^{\circ}C$, while there are no significant variations in the phase of more than $0^{\circ}C$, with an almost stable demand. Washbowl water shows the highest and toilet water shows the lowest relation with temperature in correlation analysis results. In the results of ANOVA to find the significant difference in each unit water use by exogenous factors such as housing type, occupation, number of generation, residential area and income et al., difference was shown in bathtub water by housing type and shown in kitchen, toilet and miscellaneous water by numbers of resident. Especially, definite differences in components except washbowl and bathtub water, could be found by numbers of resident. Based on the result, average residents in a house should be carefully considered and the results can be applied as reference information, in decision making process for predicting water demand and establishing water conservation policy. It is expected that these can be used as design factors in planning stage for water and wastewater facilities.

• Journal of the Korean Regional Science Association
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• v.8 no.1
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• pp.31-50
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• 1992

The aim of this paper is to define major factors influencing land development of each of major uses (residential, commercial, industrial) in the process of rapid urban expansion. The main hypothesis of this study is that land use changing patterns are directed by supply side of land managed to public policies rather than demand side. The graphic analysis is applied to relationships between urban growth and land development process of each use and between land development project managed to public policies and land development process. Public and land development projects and zonning protection seem to be major roles of land supply and main determinants of urban spatial structure. Location factors for land development of each uses are selected in 23 variables. Factor analysis is applied to test correlation between variables in 1971 and 1981. Factor structure between two years is similar, but progressive processing of functional separation is derived such as intensive land use is grouped, different location between residential and industrial use is deep. Dependent variables are standardized to logarithm of land development of each use per unit vacant land in two periods, between 1971 and 1980 year and between 1981 year. Correlation analysis between 6 dependent variables and 23 location factors in each years are applied. Major factors of each use are selected in criteria such as high correlation with dependent variables, low correlation between independent variables and common application in two periods. As the result, major factors for residential land development are Land Readjustment Project (LRP), percent of total zoned area in residential zone, residential floor space density per available area, percent of total area in industrial use; for commercial development is distance to CBD, percent of total area in commercial use, residential floor space density per available area in each year, and volumn rate of industrial use; for industrial use is percent of total area of industrial use is percent of total area of industrial use, Industrial Estate Project (IES), LRP, and distance from CBD. Land development pattern of each use between two periods are slightly different. So 6 equation is derived from appling backward method of regession. Adjusted multiple R squares of all is more than 0.5 and those equation is statistically significant and valuable to assist urban land use forecasting.