• 대한교통학회지
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• 제7권2호
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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.

• 한국농공학회논문집
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• 제51권6호
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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.

• 스마트미디어저널
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• 제8권4호
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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.

• 경영과학
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• 제26권1호
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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.

• 통합자연과학논문집
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• 제15권2호
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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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• 제16권1호
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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.

• 농업과학연구
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• 제49권2호
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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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• 제26권4호
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• pp.885-894
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• 2015

전력시스템의 안정적이고 효과적인 운영을 위해선 전력수요예측이 필요하다. 본 연구에서는 일별전력수요패턴의 시간에 따른 커브를 군집분석 하려고 한다. 2009년 1월 1일부터 2011년 12월 31일까지의 일별 시간단위 전력수요 자료는 추세성분 제거와 로그변환을 통해 계절성분과 오차성분으로 구성된 시계열자료로 변환되었다. 변환된 자료는 Ma 등 (2006)이 제안한 함수적 군집모형을 사용하여 분석되었고, 모수는 EM알고리즘과 일반화교차검정을 통해 추정되었다. 군집의 수는 휴일과 평일을 잘 분류하는 10개로 결정하였다. 분석결과 월요일, 평일 (화요일~금요일), 토요일, 일요일 또는 공휴일과 계절요인으로 전력수요 평균곡선이 설명된다. 함수적 군집분석을 통한 전력수요패턴의 과학적인 분류는 향후 단기전력수요예측에 활용된다.

• 자원ㆍ환경경제연구
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• 제17권2호
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• pp.213-233
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• 2008

현재 한국전력이 시행하고 있는 하절기 부하관리 프로그램의 성과를 평가함에 있어, 해당 프로그램이 시행되지 않은 상태의 수요가 확인되지 않은 상태에서 단순 합산한 것임에도 불구하고 이 크기의 전체를 수요관리 효과로 집계하고 있다. 본 연구는 이러한 문제에 대해, 이들 프로그램 중에서 가장 비중이 큰 두 가지 프로그램-휴가보수기간 조정제도와 자율절전제도-이 하절기 피크 시간대인 오후 2시~4시 사이의 전력수요에 얼마나 큰 영향을 미치는지를 계량적으로 분석한다. 연구 결과에 의하면 이들 두 제도의 효과를 따로 분석할 경우에는 휴가보수기간 조정제도는 보된 실적치 1kW당 실제로 피크 시간대 전력수요가 시간당 0.53kW만큼 감소하였으며, 자율절전의 경우 실적치 1kW당 피크 시간대 전력수요는 시간당 0.57kwh만큼 감소하였다. 이들 결과는 이들 수요관리 프로그램들이 실제로 피크부하를 감소시키는 데에 상당한 효과가 있음을 보여 준다. 그러나 이 두 제도의 효과를 한꺼번에 분석하는 경우에는 각각의 효과가 크게 약해짐을 볼 수 있었다.

• 재활간호학회지
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• 제6권2호
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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.