• 한국수자원학회논문집
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• 제46권7호
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• pp.755-765
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• 2013

본 연구의 목적은 앙상블 칼만필터링 기법과 연속형 강우-유출모형을 연계한 SURF 모형과Auto ROM을 결합한 실시간 댐 수문량 예측모형(DHVPM)을 개발하고 그 적용성을 평가하는데 있다. 대상유역은 충주댐 상류유역을 선정하였으며 2006~2009년 동안 연최대 유입량이 발생한 4개 사례를 선정하였다. 관측유량 자료동화 적용에 따른 선행시간 1시간 유입량에 대한 첨두유량 상대오차, 평균제곱근오차, 모형효율성계수를 산정한 결과, 2007년 첨두유량 상대오차 결과를 제외한 모든 사례에서 자료동화기법을 적용한 결과가 우수한 것으로 나타났다. 현시점으로 가정한 가상시점에서 예측 선행시간 10시간에 대해 유입량을 예측한 결과에서, 유역평균 강우량의 오차가 큰 경우에 대해 자료동화기법을 적용함으로써 예측 유입량의 오차가 줄어드는 것을 확인하였다. 이상의 결과로부터 실시간 예측유입량의 정확도를 향상시키기 위해서는 관측유입량의 실시간 활용이 가능한 환경에서 자료동화기법을 연계한 유입량 예측모형을 이용하는 것이 바람직할 것으로 판단된다.

• 상하수도학회지
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• 제19권4호
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• 한국컴퓨터산업학회논문지
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• 제6권2호
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• pp.241-248
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• 2005

기존의 예측 방법들은 과거의 통계적인 수치를 사용해서 미래를 예측했었다. 정확하게 농산물 가격을 예측하려면 정확한 지식과 많은 노력이 필요하다. 그러므로 이러한 문제점을 해결하기 위해서, 본 논문에서는 농산물 예측 가격을 향상하기 위해서 전처리로 퍼지 및 신경망을 사용하였다. 또한 후처리로써 예기치 못한 상황을 실시간으로 예측할 수 있는 지능형 농사 전문가시스템을 개발하였다. 시뮬레이션결과 제안된 농산물 가격 예측이 퍼지 규칙을 사용하지 않은 기존 수요예측 시스템보다 가격오차를 줄일 수 있음을 입증했다.

• 보건행정학회지
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• 제13권3호
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• pp.91-103
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• 2003

The purpose of this study was to provide basic reference data for stabilization scheme of health expenditure through forecasting of health expenditure. The authors analyzed the health expenditure from 1985 to 2000 that had been calculated by Korean institute for health and social affair using transfer function model as ARIMA model with input series. They used GDP as the input series for more precise forecasting. The model of error term was identified ARIMA(2,2,0) and Portmanteau statics of residuals was not significant. Forecasting health expenditure as percent of GDP at 2010 was 6.8%, under assumption of 5% GDP increase rate. Moreover that was 7.4%, under assumption of 3% GDP increase rate and that was 6.4%, under assumption of 7% GDP increase rate.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.295-297
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• 2000

In case of power demand forecasting the most important problem is to deal with the load of special-days, Accordingly, this paper presents a method that forecasting special-days load with regression models and neural networks. Special-days load in summer season was forecasted by the multiple regression models using weekday change ratio Neural networks models uses pattern conversion ratio, and orthogonal polynomial models was directly forecasted using past special-days load data. forecasting result obtains % forecast error of about $1{\sim}2[%]$. Therefore, it is possible to forecast long and short special-days load.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2003년도 학술발표논문집
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• pp.535-538
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• 2003

A Grey model was developed to forecast short-term runoff from the Naju watershed in Korea. In calibration, the root mean square error(RMSE) of the simulated runoff of six hours ahead using Grey model ranged from 6.3 to $290.52m^3/s,\;R^2$ ranged from 0.91 to 0.99, compared to the observed data. In verification, the RMSE ranged from 75.7 to $218.9m^3/s,\;R^2$ ranged from 0.87 to 0.96, compared to the observed data. The results in this study demonstrate that the proposed model can reasonably forecast runoff one to six hours ahead.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.86-94
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• 2018

This study focuses on presenting the IT program module provided by real - time forecasting and database of the voluntary disclosure quality measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. This study suggests a model of the algorithm that the quality of real - time voluntary disclosure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine.

• 에너지공학
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• 제6권1호
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• pp.68-76
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• 1997

본 논문에서는 단기 부하예측을 위하여 인공신경망 모형을 제안하였다. 본 논문에서 제안된 인공신경망의 학습알고리즘은 기존의 역전파 알고리즘 보다 효과적으로 학습수렴이 빠르며 모수결정과 초기가중치 값들에 대한 의존도가 낮은 동적 적응 학습알고리즘을 개발하여 단기 부하예측에 그 적용 가능성을 시험하였다.

• 한국조리학회지
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• 제5권1호
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• pp.89-101
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• 1999

This study attempts to identify the most accurate quantitative forecasting technique for measuring the future level of demand for food ＆ beverage in super deluxe hotel in Seoul, which will subsequently lead to determining the optimal level of purchasing food ＆ beverage. This study, in detail, examines the food purchasing system of H hotel, reviews three rigorous univariate time series models and identify the most accurate forecasting technique. The monthly data ranging from January 1990 to December 1997 (96 observations) were used for the empirical analysis and the 1998 data were left for the comparison with the ex post forecast results. In order to measure the accuracy, MAPE, MAD and RMSE were used as criteria. In this study, Box-Jenkins model was turned out to be the most accurate technique for forecasting hotel food ＆ beverage demand among selected models generating 3.8％ forecast error in average.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1144-1148
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• 1997

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.