• 한국해안·해양공학회논문집
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• 제34권6호
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• pp.188-197
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• 2022

전지구수치예보모델의 해상풍 예측자료를 기반으로 비정형격자의 국지연안 파랑예측시스템을 구축하고, 파랑모델의 수행성능 및 예측성능을 검증하였다. 기존의 정형격자는 복잡한 해안선과 연안지형에서의 파랑예측이 제한적이기 때문에 정밀한 국지연안 수치모의를 위해 비정형격자체계를 적용하고, 현업 예보 지원에 대한 적용가능성을 검토하였다. 두 격자체계 모두 근해와 연안에서 유사한 예측경향을 보였고, 격자체계에 따른 예측오차의 차이도 크지 않았다. 또한 정형격자와 비교하여, 비정형격자의 모델수행시간이 동일한 조건에서 현저히 감소하는 것을 통해 비정형격자 기반 파랑예측시스템의 현업 예보 지원에 대한 적용가능성을 확인하였다.

• 대기
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• 제26권4호
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• pp.599-615
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• 2016

This study investigates the accuracy of short-term ocean predictions during the development of ocean stratification for the Korea Meteorological Administration (KMA) Global Seasonal Forecast System version 5 (GloSea5) as well as the effect of atmosphere-ocean coupling on the predictions through a series of sensitive numerical experiments. Model performance is evaluated using the marine meteorological buoys at seas around the Korean peninsular (KP), Tropical Atmosphere Ocean project (TAO) buoys over the tropical Pacific ocean, and ARGO floats data over the western North Pacific for boreal winter (February) and spring (May). Sensitive experiments are conducted using an ocean-atmosphere coupled model (i.e., GloSea5) and an uncoupled ocean model (Nucleus for European Modelling of the Ocean, NEMO) and their results are compared. The verification results revealed an overall good performance for the SST predictions over the tropical Pacific ocean and near the Korean marginal seas, in which the Root Mean Square Errors (RMSE) were $0.31{\sim}0.45^{\circ}C$ and $0.74{\sim}1.11^{\circ}C$ respectively, except oceanic front regions with large spatial and temporal SST variations (the maximum error reached up to $3^{\circ}C$). The sensitive numerical experiments showed that GloSea5 outperformed NEMO over the tropical Pacific in terms of bias and RMSE analysis, while NEMO outperformed GloSea5 near the KP regions. These results suggest that the atmosphere-ocean coupling substantially influences the short-term ocean forecast over the tropical Pacific, while other factors such as atmospheric forcing and the accuracy of simulated local current are more important than the coupling effect for the KP regions being far from tropics during the development of ocean stratification.

• 디지털융복합연구
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• 제16권10호
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• pp.125-132
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• 2018

본 연구는 경영자 능력의 대용치로 경영자가 자발적으로 공시하는 미래 기업의 성과에 대한 정보인 이익 예측 정확성을 사용하여, 미래 이익을 정확하게 예측하는 경영자의 우수한 능력이 높을수록 경영자 성과-보상에 어떠한 영향을 미치는지에 대해 분석하고자 한다. 본 연구의 분석결과, 이익 예측 정확성과 경영자 보상 사이의 유의한 양(+)의 관계가 나타났으며, 이는 미래에 대한 예측이 우수한 경영자의 능력을 경영자 보상 계약에 반영한 결과로 볼 수 있다. 본 연구는 기존 선행연구에서 경영자의 능력의 대용치로 회계성과 변수를 주로 사용하여 경영자 보상 계약을 확인한 것을 확장하여 미래 기업이 직면할 상황을 정확하게 예측하는 경영자의 능력이 경영자 보상에 영향을 미치는 주요한 결정 요인임을 검증하였다는 것에 차별성이 존재한다. 따라서 기업의 미래에 대한 예측 역시 중요한 경영자의 역량으로 경영자 보상 계약에 영향을 미치는 추가적인 결정요인을 파악했다는 것에 의의가 있다.

• 플랜트 저널
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• 제15권1호
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• pp.31-37
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• 2019

본 연구에서는 지역난방시스템 열수요 예측의 정확도 향상을 위하여 판교지역을 대상으로 지역난방 수요예측 주요인자 중 열수요 실적을 기존의 열원시설 열공급정보 대신 변경된 사용자시설 열판매정보로 적용하여 혹한기를 포함한 5개월 동안의 수요를 예측하고 실적값을 기준으로 기존 방식과 정확도를 비교하였다. 열수요가 피크를 이루는 혹한기 1주일(2018.01.08.~01.14) 동안 실적값을 기준으로 기존 및 변경방식 예측값의 시간대별 차이를 비교한 결과 상대오차가 7%에서 3%로 감소되었으며, 2017년 10월부터 2018년 2월까지 5개월에 걸친 일일 누적 열수요에 있어서도 실적값 대비 기존 및 변경 방식 예측값의 상대오차는 각각 9%와 4%로 변경방식의 상대오차가 감소하였다. 또한, 열수요 특성이 차별화되는 주말의 경우에도 예측값의 상대오차는 기존 방식 10%에서 변경 방식 5%로 일관성 있게 감소함을 확인할 수 있었다.

• 대기
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• 제18권3호
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• pp.237-248
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• 2008

A thermal index which considers metabolic heat generation of human body is proposed for operational forecasting. The new thermal index, Perceived Temperature (PT), is forecasted using Weather Research and Forecasting (WRF) mesoscale model and validated. Forecasted PT shows the characteristics of diurnal variation and topographic and latitudinal effect. Statistical skill scores such as correlation, bias, and RMSE are employed for objective verification of PT and input meteorological variables which are used for calculating PT. Verification result indicates that the accuracy of air temperature and wind forecast is higher in the initial forecast time, while relative humidity is improved as the forecast time increases. The forecasted PT during 2007 summer is lower than PT calculated by observation data. The predicted PT has a minimum Root-Mean-Square-Error (RMSE) of $7-8^{\circ}C$ at 9-18 hour forecast. Spatial distribution of PT shows that it is overestimated in western region, while PT in middle-eastern region is underestimated due to strong wind and low temperature forecast. Underestimation of wind speed and overestimation of relative humidity have caused higher PT than observation in southern region. The predicted PT from the mesoscale model gives appropriate information as a thermal index forecast. This study suggests that forecasted PT is applicable to the prediction of health warning based on the relationship between PT and mortality.

• 대한교통학회지
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• 제12권1호
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• pp.25-42
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• 1994

The traffic forecast is one of the most important analysis objects in the urban transportation planning process. The results of traffic forecast are the most widely used informations and give a critical influence on the major decision makings in the transportation planning process. Thus, they should be as much accurate and credible data, and evaluated to determine whether they are enough reliable to directly use in the planning process. However, the evaluation process is usually overlooked or abbreviated with a few exceptions according to the size and character of the project. Even though a planner or engineer tries to evaluate the assignment results, he/she is usually faced with certain difficulties since there are no established criteria and methods for the accuracy evaluation. Accordingly, the main purpose of this research placed on establishing the criteria and methods for the accuracy evaluation of the assignment results. The secondary purpose was to evaluate which assignment technique produces the most accurate assignment results by applying the established evaluation criteria and methods to an actual network. The research found that the proposed evaluation methods well operated in testing the accuracy of assignment results with few limits on application. Also, the incremental assignment was found to provide the best assignment results of existing assignment techniques (Stochastic, Iterative, Incremental, Equilibrium assignment) for the Seoul city network applied.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.280-282
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• 2021

주관적 판단을 적용하여 예보되는 미세먼지 예보의 문제를 해결하기 위해 딥러닝 알고리즘을 이용하여 미세먼지 예측 모델을 설계하였다. 딥러닝 알고리즘 중 RNN과 LSTM을 이용하였으며, 하이퍼 파라미터 탐색을 통해 최적의 파라미터를 적용하여 설계하였다. RMSE와 예측 정확도를 통해 두 모델의 예측 성능을 평가하였다. 성능 평가 결과, RMSE와 전체 정확도에서 큰 차이는 없었으나 세부 예측 정확도의 차이가 있음을 확인하였다.

• 한국국방경영분석학회지
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• 제8권1호
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• pp.99-107
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• 1982

This paper is concerned with the optimality of exponential smoothing applied to the general IMA process with different moving average and differencing orders. Numerical experiments were performed for IMA(m,n) process with various combinations of m and n, and the corresponding forecast errors were compared. Results show that the higher differencing order is more critical to the optimality of exponential smoothing, i.e., the IMA process with the higher moving average order, forecasted by exponential smoothing, has comparatively smaller forecast error. If the difference between the differencing order and the moving average order becomes larger, the accuracy of forecast by exponential smoothing declines gradually.

• Nuclear Engineering and Technology
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• 제52권6호
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• pp.1334-1339
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• 2020

This paper presents an empirical and comprehensive forecasting analysis of the uranium price. Prices are generally difficult to forecast, and the uranium price is not an exception because it is affected by many external factors, apart from imbalances between demand and supply. Therefore, a systematic analysis of multiple forecasting methods and combinations of them along repeated forecast origins is a way of discerning which method is most suitable. Results suggest that i) some sophisticated methods do not improve upon the Naïve's (horizontal) forecast and ii) Unobserved Components methods are the most powerful, although the gain in accuracy is not big. These two facts together imply that uranium prices are undoubtedly subject to many uncertainties.

• 전기학회논문지
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• 제65권6호
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• pp.918-922
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• 2016

As the number of wind farms operating has increased, the interest of the central unit commitment and dispatch for wind power has increased as well. Wind power forecast is necessary for effective power system management and operation with high wind power penetrations. This paper presents the centralized wind power forecasting method, which is a forecast to combine all wind farms in the area into one, using time series models. Also, this paper proposes a prediction model modified with wind forecast error compensation. To demonstrate the improvement of wind power forecasting accuracy, the proposed method is compared with persistence model and new reference model which are commonly used as reference in wind power forecasting using Jeju Island data. The results of case studies are presented to show the effectiveness of the proposed wind power forecasting method.