• Environmental Engineering Research
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• 제24권3호
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• 한국철도학회논문집
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• 제7권2호
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• pp.93-98
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• 2004

For the reduction and efficient management of railway noise, first of all prediction of railway noise is necessarily requisted. At home and abroad many studies for prediction of railway nearby noise have been accomplished. But it is impossible to predict exactly for the Korean Railway, because the acoustic powers for each rolling stock used in Korea have not been built yet. So in this study, acoustic powers for each Korean rolling stock such as Samaeul, Mugungwha were builded acceding to the speed and rail support systems. Predicted results using the acoustic powers suggested in this study are compared with measured results and it is known that these acoustic powers can be used for precise prediction of railway noise.

• 조명전기설비학회논문지
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• 제29권5호
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• pp.94-99
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• 2015

Prediction algorithm of the energy storage system in accordance with the load pattern can cause economic loss in case of a failure prediction. In addition, algorithm that uses TOU(Time of Use) based on the revelation by the power electric charge which covers most simply is an inefficient operation because it is only for the purpose of reducing the peak power. In this paper, we introduced a non-prediction algorithm with a conventional TOU in order to solve this problem operating the energy storage system economic and efficient.

• 한국자동차공학회논문집
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• 제3권6호
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• pp.212-223
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• 1995

To improve the efficiency of a power transmission system with slip elements, power split/circulation system is applied. The performance of a power split/circulation system varies widely by the change of the followings; the layout of system, the type and gear ratio of planetary gear, the performance of slip element, etc. Therefore, when one designs such a power transmission system or when one determines the economic/power mode of system, a certain performance prediction method is needed. In this study, the internal power flow pattern of a power split/circulation system is theoretically analyzed on several transmission systems. And an effective performance prediction method(so called performance locus diagram) is presented. By this method, the effects of design factors can be easily understood and the qualitative performances of system can be clearly evaluated.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1504-1514
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• 2018

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%.

• 한국태양에너지학회 논문집
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• 제36권1호
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• pp.27-37
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• 2016

A simple but practical Ensemble Prediction System(EPS) for wind power forecasting was developed and evaluated using the measurement of the offshore meteorological tower, HeMOSU-1(Herald of Meteorological and Oceanographic Special Unite-1) installed at the Southwest Offshore in South Korea. The EPS developed by the Korea Institute of Energy Research is based on a simple ensemble mean of two Numerical Weather Prediction(NWP) models, WRF-NMM and WRF-ARW. In addition, the Kalman Filter is applied for real-time quality improvement of wind ensembles. All forecasts with EPS were analyzed in comparison with the HeMOSU-1 measurements at 97 m above sea level during Typhoon Bolaven episode in August 2012. The results indicate that EPS was in the best agreement with the in-situ measurement regarding (peak) wind speed and cut-out speed incidence. The RMSE of wind speed was 1.44 m/s while the incidence time lag of cut-out wind speed was 0 hour, which means that the EPS properly predicted a development and its movement. The duration of cut-out wind speed period by the EPS was also acceptable. This study is anticipated to provide a useful quantitative guide and information for a large-scale offshore wind farm operation in the decision making of wind turbine control especially during a typhoon episode.

• 플랜트 저널
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• 제15권2호
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• pp.56-61
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• 2019

본 연구에서는 500 MW급 초임계압 석탄 화력발전소의 발전보일러용 고압 급수가열기에서 발생한 내부 튜브의 파손 사례 분석을 통해 운전 기록 모니터링에 의한 발전보일러용 고압 급수가열기 내부 튜브의 파손 예측 방안을 모색하고자 하였다. 이 연구를 통해 고압 급수가열기 내부 튜브 파손 시 쉘 측 수위 조절 밸브 개도와 보일러 급수펌프 흡입 유량의 변화로 내부 튜브 파손을 진단할 수 있는 예측 모형을 제안하였고, 제안된 예측 모형은 급수 계통의 불균형이 일어난 추가 사례를 통해 실증하였다. 이에 따라 본 연구와 유사한 특성의 발전보일러용 고압 급수가열기의 경우에도 쉘 측 수위 조절 밸브 개도와 보일러 급수펌프의 흡입 유량의 정상 운전 상태 값 대비 현재 운전 값 비교는 고압 급수가열기 내부 튜브의 파손에 대한 유력한 예측 진단 방안이 될 수 있다고 판단된다.

• 한국음향학회지
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• 제21권8호
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• pp.703-707
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• 2002

새로운 콘트롤 밸브 소음 예측 방법을 제안하고 밸브 및 배관으로 이루어진 시스템에 적용하여 그 가능성을 할인하였다. 기존의 소음 예측 방법은 무향실을 이용하는 기존의 예측방법보다 일반 산업체에서 시험 시설을 설치하기가 용이한 장점이 있다. 측정된 음향파워를 이용하여 소음예측식을 결정하고 상대유량계수 0.11인 경우 소음예측식에 따라 유량 및 차압의 변화에 따른 음향파워레벨을 평가하였고 그 결과가 측정값과 잘 일치함을 확인하였다. 제안된 밸브소음 예측기법은 콘트롤밸브의 소음특성을 나타내는 지표로서 사용할 수 있다.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.1342-1348
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• 2015

The objective of this study is to propose a method to calculate prediction intervals for one-day-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.

• Journal of Platform Technology
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• 제6권2호
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• pp.19-25
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• 2018

기존의 태양광 발전 모니터링 시스템은 현재의 발전량, 과거의 발전량, 환경센서 값등을 모니터링 한다. 이는 발전소의 효율적인 운영과 유지보수를 위한 태양광 발전량 예측이 필요하기 때문이다. 이를 위해 데이터 축적을 통해 빅데이터 기반 태양광 발전 모니터링 시스템의 발전량 예측 알고리즘 구현 방안을 제안한다. 이를 통해 미리 예측된 발전량에 비례하여, 태양광 발전 플랜트의 고장을 예측하고자 한다. 결과적으로 시스템의 고장을 예측하여 미리 점검하도록 한다.