• Journal of Korean Society of Transportation
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• v.7 no.2
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• pp.29-43
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• 1989

The purpose of this study is to construct a forecasting model involved in a diverted traffic volume of the 2nd intra-urban expressway in construction presently, in the case of the future prediction of traffic demand for the intra-urban expressway in Pusan. In this study, the model involved in a diverted traffic volume is constructed trustworthy. And the future traffic demand of intra-urban expressway by this model was forecasted 114,005 volume/daily in 1996 and 147,090 volume/daily in 2001. However, it will made a study more and more concretely for practicality and limitation as well as construction of the forecasting model considered an intrinsic problem of an observational error and necessity of survey for much more socio-economic data, the traffic volume on all orad and OD pairs in Pusan.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.6
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• pp.25-31
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• 2009

In this study, pollutant loads delivery ratio by flow duration in Hwangryoung A watershed was estimated. The delivery ratio was estimated with measured data by Ministry of Environment(MOE) and the regression equation based on geomorphic parameters. Eight day interval flow data measured by the MOE were converted to daily flow to calculate daily load and flow duration curve by correlating data of neighboring station which has daily flow data. Regression equation developed by previous study was tested to study watershed and found to be satisfactory. The delivery ratios estimated by two methods were compared. For the case of Biochemical oxygen demand(BOD), the delivery ratios of low flow condition were 7.6 and 15.5% by measured and regression equation, respectively. Also, the delivery ratios of Total phosphorus(T-P) for normal flow condition were 13.3 and 6.3% by measured and regression equation, respectively.

• Smart Media Journal
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• v.8 no.4
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• pp.72-79
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• 2019

The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers' willingness to participate in the program.

• Korean Management Science Review
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• v.26 no.1
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• Journal of Integrative Natural Science
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• v.15 no.2
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• pp.89-97
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• 2022

This paper presents the comprehensive feasibility study of solar mini-grid project located in Bajhang District, Sudur Paschim Province, Nepal. The study has been conducted with the aim of developing a suitable size solar mini-grid system to meet electricity demand of proposed settlements of the village people. The study forecasts that the estimated average daily peak power consumption of load is about 20kW and average daily energy demand of load is about 100-150kWh/day in the base year 2022. The shared ratio of productive end uses is about 25% of the total power consumption and about 27% of the total energy demand, which will be used for small business/income generation activities and required 45kWp size solar power generation mini-grid system. The estimated project cost for the proposed 45kW solar mini-grid system technology, including 3 years of operation & maintenance, as well as power distribution network up to end user's premises is about 0.24 million USD. It is concluded that 45kWp photovoltaic mini-grid is feasible for the location.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.885-894
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• 2015

It is necessary to forecast the electricity demand for reliable and effective operation of the power system. In this study, we try to categorize a functional data, the mean curve in accordance with the time of daily power demand pattern. The data were collected between January 1, 2009 and December 31, 2011. And it were converted to time series data consisting of seasonal components and error component through log transformation and removing trend. Functional clustering by Ma et al. (2006) are applied and parameters are estimated using EM algorithm and generalized cross validation. The number of clusters is determined by classifying holidays or weekdays. Monday, weekday (Tuesday to Friday), Saturday, Sunday or holiday and season are described the mean curve of daily power demand pattern.

• Environmental and Resource Economics Review
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• v.17 no.2
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• pp.213-233
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• 2008

In this paper, we estimate the effects of the two most important means of summer time demand side management in Korean power market: adjustment of vacation or repair timing and the voluntary saving program. We use regression analyses to estimate how effective these two programs are in reducing the peak time demand during the summer. Our results show that adjustment of vacation or repair timing actually reduces the daily peak demand by 0.53 kWh per one kWh reported reduction calculated from the agreements between Kepco and the users. The voluntary saving program reduces the daily peak by 0.57 kWh per one kWh reported reduction calculated from the agreements between Kepco and the users. However, when we include these two effects in the same regression model, their respective estimated effects become much weaker.

• The Korean Journal of Rehabilitation Nursing
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• v.6 no.2
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• pp.226-238
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• 2003

This study was started for the purpose of providing the basic data for continous managment of kidney transplantation patients after discharge. This study was conducted on 180 patients who received renal transplants at three hospital( B, M, P) pusan, korea. The data collection was done for june 1, to August 31, 2002. General characteristics, renal transplantaton characteristics, physical characteristics, the level of compliance and the degree of educational demand were done by the number and percentage, the mean, standard deviation. The level of compliance and educational demand followed by the characteristics of general and kidney transplantation were analyzed by t-test and ANOVA. The result were as fallows; 1. Man was higher than woman such as 60.0%, Mean age was 42.5 years old, Average total duration of after operation was 5.5 years. 2. Cases of systolic blood pressure over 140mmHg were 10.0%, cases of diastolic blood pressure over 90mmHg were 22.8% and obesity factor in BMI was 15.6%. The person who daily water intake amount is 5000cc was 0.6%, the case that daily urine output is below 1000cc was 8.9%, and the case that urine output is zero was 0.6%. 3. The mean score of compliance was 77.47 point, The score in medication part was highest such as 4.67 point, that in stress situation was lowest such as 3.50 point. 4. The average score of educational demand was 154.02 point, and physical state part was 4.36 points highest, activation part was 3.48 points lowest. As a role of nurse Confirmation of compliance is very important encourage to make good through regular hospital visitation, point out the noncompliance part and then increase compliance of renal transplantation patient As well there will be maintain the normal kidney function to satisfy educational demand through continous education.