• 한국컴퓨터정보학회논문지
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• 제20권3호
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• pp.19-27
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• 2015

대체 에너지 기술 개발을 위해 지난 20년 동안 풍력 발전에 관련한 기술들이 축적되어왔다. 풍력 발전은 자연적으로 부는 바람을 에너지원으로 사용하므로 환경 친화적이며 경제적이다. 이러한 풍력 발전의 효율적인 운영을 위해서는 시시각각 변하는 자연 바람의 세기를 정확도 높게 예측할 수 있어야 한다. 풍속을 평균적으로 얼마나 정확하게 잘 예측하는지도 중요하지만 실제 값과 예측 값의 절대 오차의 최댓값을 최소화시키는 것 또한 중요하다. 발전 운영 계획 측면에서 예측 풍속을 통한 예측 발전량과 실제 발전량의 차이는 경제적 손실을 가져오는 원인이 되므로 유연한 운영 계획을 세우기 위해 최대 오차가 중요한 역할을 한다. 본 논문에서는 풍속 예측 방법으로 과거 풍속 변화 추세뿐만 아니라 기상청 예보와 시기적인 풍속의 특성을 고려하기 위한 경향 값을 반영하여 수치 예측 알고리즘으로 학습한 풍속 예보 모델을 제안한다. 기상청 예보는 풍력 발전 단지를 포함하는 비교적 넓은 지역의 풍속을 예보하지만 풍속을 예측하고자 하는 국소지점에 대한 풍속 예측의 정확도를 높이는데 상당히 기여한다. 또한 풍속 변화 추세는 긴 시간동안 관측한 풍속을 세세하게 반영할수록 풍속 예측의 정확도를 높인다.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• 디지털융복합연구
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• 제12권3호
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• pp.211-218
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• 2014

최근 풍력에너지는 풍력터빈의 지능화뿐만 아니라 풍력 발전량 예측 부분에서 컴퓨팅과의 결합이 확대되고 있다. 풍력 발전은 기상상태에 따라 출력변동이 심하고 출력 예측이 어려워 효율적인 전력 생산을 위해서 신재생에너지를 전력계통에 안정적으로 연계할 수 있는 기술이 필요하다. 본 논문에서는 분산형 전원의 예측정보를 향상시켜 예측한 발전량과 실제 발전량의 차이를 최소화하기 위한 분산형 전원전력의 단기예측 모델을 설계한다. 제안된 모델은 단기 예측을 위해서 물리모델과 통계모델을 결합하였으며, 물리모델에서 생산된 격자별 예측값 중 예측 지점내 예측지점의 값을 추출하고, 물리 모델 예측값에 통계모델을 적용하여 발전량 산정을 위한 최종 기상 예측값을 생성한다. 또한, 제안 모델에서는 실시간 기상청 관측자료와 실시간 중기 예측 자료를 입력 자료로 사용하여 단기 예측모델을 수행한다.

• 동력기계공학회지
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• 제3권1호
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• pp.38-44
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• 1999

Subway is one of the most important transportation and its facilities are increased by the drift of population to cities in these days. But heat generation results from lighting, human and traffic increase in subway, half-closed space, gives uncomfortable sense to the subway passengers. Therefore, natural ventilation by piston effect is done to relieve uncomfortable sense. But train wind by piston effect gives uncomfortable sense to the subway passengers, too. So the numerical calculation of inflow and outflow amounts is important to predict thermal environment and reduce train wind. In case of actual survey of train wind in target station, the amount of train wind are about $3100m^3/train$ at the minimum, about $6000m^3/train$ at the maximum, about $4200m^3/train$ on average. When comparison between simulation for train wind prediction and actual survey for accuracy was done train wind prediction program showed similar results.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• 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.

• 산업기술연구
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• 제31권A호
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• pp.119-122
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• 2011

AWS (Automated Weather Station) wind data was used to predict the annual energy production of Gangwon wind farm having a total capacity of 98 MW in Korea. Two common wind energy prediction programs, WAsP and WindSim were used. Predictions were made for three consecutive years of 2007, 2008 and 2009 and the results were compared with the actual annual energy prediction presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm. The results from both prediction programs were close to the actual energy productions and the errors were within 10%.

• 디지털융복합연구
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• 제12권3호
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• pp.237-242
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• 2014

IT 서비스 및 컴퓨팅 자원을 기반으로 인터넷 서비스를 제공하는 클라우드 컴퓨팅이 최근 큰 관심을 받고 있다. 그러나 클라우드 컴퓨팅 시스템에 저장되는 데이터는 암호화한 후 저장되어도 기밀 정보가 유출되는 문제점이 있다. 본 논문에서는 사용자가 클라우드 컴퓨팅 시스템에서 제공되는 데이터를 제 3자가 임의로 악용하는 것을 예방하기 위한 그룹 키 관리 프로토콜을 제안한다. 제안된 프로토콜은 임의의 사용자가 원격에서 클라우드 컴퓨팅 서버에 접근할 경우 서버에 존재하는 사용자 인증 데이터베이스내 사용자 정보를 일방향 해쉬 함수와 XOR 연산을 사용하여 사용자 인증을 제공받는다. 도한 사용자의 신분확인 및 권한을 연동하여 클라우드 컴퓨팅 시스템에 불법적으로 접근하는 사용자를 탐색함으로써 클라우드 컴퓨팅의 사용자 보안 문제를 해결하고 있다.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2011년도 춘계학술대회 초록집
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• pp.59.2-59.2
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• 2011

Although there are many activities on the construction of wind farm to produce amount of power from the wind, in practice power productions are not as much as its expected capabilities. This is because a lack of both the prediction of wind resources and the aerodynamic analysis on turbines with far wake effects. In far wake region, there are velocity deficits and increases of the turbulence intensity which lead to the power losses of the next turbine and the increases of dynamic loadings which could reduce system's life. The analysis on power losses and the increases of fatigue loadings in the wind farm is needed to prevent these unwanted consequences. Therefore, in this study velocity deficits have been predicted and aerodynamic analysis on turbines in the far wake is carried out from these velocity profiles. Ainslie's eddy viscosity wake model is adopted to determine a wake velocity and aerodynamic analysis on wind turbines is predicted by the numerical methods such as blade element momentum theory(BEMT) and vortex lattice method(VLM). The results show that velocity recovery is more rapid in the wake region with higher turbulence intensity. Since the velocity deficit is larger when the turbine has higher thrust coefficient, there is a huge aerodynamic power loss at the downstream turbine.

• 한국태양에너지학회 논문집
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• 제32권1호
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• pp.1-7
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• 2012

Predictions of wind speed for six different near-shore sites were made using the NCAR (National Center for Atmospheric Research) wind data. The distances between the NCAR sites and prediction sites were varied between 40km and 150km. A well-known wind energy prediction program, WindPRO, was used. The prediction results were compared with the measured data from the AWS(Automated Weather Stations). Although the NCAR wind data were located far away from the AWS sites, the prediction errors were within 9% for all the cases. In terms of sector-wise wind energy distributions, the predictions were fairly close to the measurements, and the error in predicting main wind direction was less than $30^{\circ}$. This proves that the NCAR wind data are very useful in roughly estimating wind energy in offshore or near-shore sites where offshore wind farm might be constructed in Korea.