• 한국시스템다이내믹스연구
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• 제9권1호
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• pp.125-141
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• 2008

It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

• Water Engineering Research
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• 제3권2호
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• 전기학회논문지
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• 제63권4호
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• pp.451-454
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• 2014

Short-term load forecasting(STLF) for electric power demand is essential for stable power system operation and efficient power market operation. We improved STLF method by using hourly temperature as an input data. In order to using hourly temperature to STLF algorithm, we calculated temperature-electric power demand sensitivity through past actual data and combined this sensitivity to exponential smoothing method which is one of the STLF method. The proposed method is verified by case study for a week. The result of case study shows that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• 전기학회논문지
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• 제63권11호
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• pp.1608-1614
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• 2014

Power demand forecasting is an important factor of the peak management. This paper deals with the 15 minutes ahead load forecasting problem in a DC urban railway system. Since supplied power lines to trains are connected with parallel, the load characteristics are too complex and highly non-linear. The main idea of the proposed method for the 15 minutes ahead prediction is to use the daily load similarity accounting for the load nonlinearity. An Euclidean norm with weighted factors including loads of the neighbor substation is used for the similar load selection. The prediction value is determinated by the sum of the similar load and the correction value. The correction has applied the neural network model. The feasibility of the proposed method is exemplified through some simulations applied to the actual load data of Incheon subway system.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1999년도 Proceedings of the 1999 Annual Conference The Korean Society of Agricutural Engineers
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• pp.512-518
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• 1999

Recently rainfall and water evel are monitored via on -line system in real-time bases. We applied the on-line system to get the rainfall and waterlevel data for the development of the real-time flood forecasting model based on SCS method in hourly bases. Main parameters for the model calibration are concentration time of flood and soil moisture condition in the watershed. Other parameters of the model are based on SCS TR-%% and DAWAST model. Simplex method is used for promoting the accuracy of parameter estimation. The basic concept of the model is minimizing the error range between forcasted flood inflow and actual flood inflow, and accurately forecasting the flood discharge some hours in advance depending on the concentration time. The flood forecasting model developed was applied to the Yedang and Topjung reservoir.

• 한국농공학회지
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• 제36권3호
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• pp.60-66
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• 1994

This paper is to validate the proposed models for the real-time forecasting for the Keum river estuary dam such as tidal-level forecasting model, one-dimensional unsteady flood routing model, and Kalman filter models. The tidal-level forecasting model was based on semi-range and phase lag of four tidal constituents. The dynamic wave routing model was based on an implicit finite difference solution of the complete one-dimensional St. Venant equations of unsteady flow. The Kalman filter model was composed of a processing equation and adaptive filtering algorithm. The processng equations are second ordpr autoregressive model and autoregressive moving average model. Simulated results of the models were compared with field data and were reviewed.

• 한국수자원학회논문집
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• 제35권5호
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• pp.619-629
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• 2002

대기에서의 물순환은 기후시스템이라는 커다란 공간 안에서 다양한 인자들의 상호작용을 통하여 이루어진다. 즉, 어떠한 기후 현상도 그 자체적으로 발생할 수는 없다. 따라서, 많은 연구자들은 영향인자들의 분석을 통하여 기후 변화를 이해하고자 노력하여 왔다. 본 연구에서는 다양한 인자에 의하여 영향을 받아 발생하는 강수량의 예측을 위하여 실제 세계의 근사적이고 부정확한 성질을 표현하는데 효과적인 퍼지 개념을 이용하였다. 예측을 위하여 적용한 모형은 크게 뉴로-퍼지 시스템과 마코프 연쇄이며, 일리노이주의 강수량 예측을 위하여 적용하였다. 예측은 강수량에 영향을 끼치는 다양한 대기순환 인자(예: 토양수분과 기온)를 고려하여 수행하였다. 예측 결과, 강수량 예측에 대기순환 인자들을 고려함으로써 모형의 예측능력을 향상시킬 수 있었고, 상대적으로 뉴로-퍼지 시스템의 예측이 보다 정확한 결과를 주었다.

• 한국환경과학회지
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• 제26권10호
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• pp.1167-1180
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• 2017

Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

• 대한전기학회논문지:전력기술부문A
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• 제49권3호
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• pp.107-117
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• 2000

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2003년도 춘계공동학술대회논문집
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• pp.51-59
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• 2003

해양사고 예보 시스템(MCFS)은 해양사고의 예측건수와 위험수준을 일기예보와 같이 방송하기 위한 것이다. MCFS는 해양사고 수량화 D/B, 예측 모델, 3차원 통계 가시화 시스템 등으로 구성되어 있다. 이 논문에서는 수량화 D/B의 구현 절차를 기술했다. 해양사고 데이터는 1990년부터 2000년까지 11년간 위도 33$^{\circ}$N~35$^{\circ}$N와 경도124$^{\circ}$E~127$^{\circ}$E의 대한민국 서남해안 일대에서 발생한 총 724건을 수집하였다. 수량화 D/B의 분석방법을 제안하고 그 유효성을 검토하였다.