• 대한전기학회논문지:전력기술부문A
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• 제55권7호
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• pp.273-280
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• 2006

This paper suggests the method that forecasts Dynamic Line Rating (DLR). Thermal Overload Risk Probability (TORP) of the next time is forecasted based on the present weather conditions and DLR value by Monte Carlo Simulation (MCS). To model weather elements of transmission line for MCS process, this paper will propose the use of statistical weather models that time series is applied. Also, through the case study, it is confirmed that the forecasted TORP can be utilized as a criterion that decides DLR of next time. In short, proposed method may be used usefully to keep security and reliability of transmission line by forecasting transmission capacity of the next time.

• 한국수자원학회논문집
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• 제48권12호
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• pp.995-1009
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• 2015

초단기 홍수예보를 위한 주요자료로서 최근 기상레이더의 중요성이 크게 부각되고 있다. 기상레이더는 넓은 지역에 걸쳐 실시간으로 강우현상 감시가 가능하며 지상우량계로는 파악이 불가능한 미계측유역을 통과하는 강우장의 이동 및 변화 파악이 가능한 장점이 있다. 본 연구는 강우장의 공간적 분포와 레이더 강우세포를 추적하는 강우장 예측 해석방안을 수립하였다. 이를 위해 강우장의 공간적인 이동을 고려하기 위해 강우장의 바람장 이류(advection) 패턴을 추출하여 각 강우세포가 가지는 이동방향 및 속도를 고려한 강우장 추적기법을 통하여 강우장을 예측하였다. 본 연구를 통하여 개발된 기상레이더 강우장 상관분석 기법을 활용한 초단기강우예측 결과는 집중호우시 홍수 예 경보를 위한 수문모형의 입력자료로 활용이 가능할 것으로 사료된다.

• 한국항행학회논문지
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• 제21권6호
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• pp.643-650
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• 2017

무한한 에너지원을 가진 태양광 발전은 기상 에 의존하기 때문에 발전량이 매우 간헐적이다. 따라서 태양광 발전량의 불확실성을 줄이고 경제성을 향상시키기 위하여 정확한 발전량 예측기술이 필요하다. 기상청은 3일간 기상정보를 예보하지만 태양광 발전 예측에 높은 상관관계가 있는 일조량과 일사량은 예보하지 않는다. 본 연구에서는 기상청에서 3일간 예보하는 기상요소인 기온, 강수량, 풍향, 풍속, 습도, 운량 등을 이용하여, 일조 및 일사량을 예측하였으며, 예측된 일사 및 일조량을 이용하여, 실시간 태양광 발전량을 예측하는 딥러닝 모델을 제안하였다. 결과로서 예측된 기상요소로 발전량을 예측하는 모델보다 제안 모델이 MAE, RMSE, MAPE 등의 오차율 지표에서 더 좋은 결과를 보여주었다. 또한, 기계 학습의 한 종류인 서포트 벡터 머신을 사용하는 것보다 DNN을 사용하는 것이 더 낮은 오차율 지표를 보여주었다.

• 한국융합학회논문지
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• 제8권10호
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• pp.1-8
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• 2017

정확한 예측은 미래에 일어날 현상에 대해 효과적으로 준비 혹은 대처 할 수 있게 해준다. 특히, 기상 현상은 인간의 생활과 밀접한 연관이 있으며, 발생할 수 있는 기상 및 재난 예측을 통해 인명, 재산 등의 피해로부터 예방 할 수 있게 해준다. 해상에서 발생할 수 있는 재난 중 하나인 유류유출 사고에 대해 빠르고 효과적으로 대응하기 위해서는 유출유의 이동과 주변 해역의 기상을 정확하게 예측하는 것이 중요하다. 본 논문에서는 분류 및 회귀 예측과 관련된 연구에서 준수한 성능 및 예측 가능성을 보여준 기계학습 기법으로 서포트 벡터 머신, 가우시안 프로세스, 다층 퍼셉트론, 방사기저함수 네트워크의 총 4 개의 기계학습 기법을 선별하였다. 선별한 기계학습 기법을 이용하여 유류유출의 탐지와 바람, 강우량, 오존 등의 기상 데이터를 예측하는 연구들의 연구 방법과 결과 등을 설명하며 이를 활용한 기계학습 기반 유류유출 예측 모델의 적용 가능성을 제시한다.

• 대기
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• 제26권1호
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.127-131
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• 1999

Typhoons were known by contributing to transporting plus heat or kinetic energy from equatorial region to midlatitude region. Due to the strong damage from typhoon, we acknowledged the theoretical study and the importance of accurate forecast about typhoon. In this study, typhoon forecasting system was developed to search the tracks of past typhoons or to display similar track of past typhoon in comparison with the path of current forecasting typhoon. It was programmed using Interactive Data Language(IDL), which was a complete computing environment for the interactive analysis and visualization of data. Typhoon forecasting system was also included satellite image and auxiliary chart. IR, Water Vapor, Visible satellite images helped users analyze an accurate forecast of typhoon. They were further refined the procedures for generating water vapor winds and gave an initial indication of their utility for numerical weather prediction(NWP), in particular for typhoon track forecasting where they could provide important information. They were also available for its utility in typhoon tracer or intensity.

• 산업공학
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• 제24권4호
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• 한국멀티미디어학회논문지
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• 제22권9호
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1300-1312
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• 2019

BSTRACT In this paper, PM10 forecast model using DNN(Deep Neural Network) is developed for Seoul region. The previous Julian forecast model has been developed using weather and air quality data of Seoul region only. This model gives excellent results for accuracy and false alarm rates, but poor result for POD(Probability of Detection). To solve this problem, an WA(Wide Area) forecasting model that uses Chinese data is developed. The data is highly correlated with the emergence of high concentrations of PM10 in Korea. As a result, the WA model shows better accuracy, and POD improving of 3%(D+0), 21%(D+1), and 36%(D+2) for each forecast period compared with the Julian model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.495-497
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• 1992

Hourly load forecasting has become indispensable for practical simulation of electric power system as the system become larger and more complicated. To forecast the future hourly load the cyclic behavior of electric load which follows seasonal weather, day or week and office hours is to be analyzed so that the trend of the recent behavioral change can be extrapolated for the short term. For the long term, on the other hand, the changes in the infra-structure of each electricity consumer groups should be assessed. In this paper the concept and process of hourly load forecasting for hourly load is introduced.