• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1548-1555
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• 2016

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive - moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

• International Journal of Advanced Culture Technology
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• 제7권4호
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.559-564
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• 2013

In this paper a new hybrid forecast method composed of wavelet transform and neural network is proposed to forecast the wind speed more accurately. In the field of wind energy research, accurate forecast of wind speed is a challenging task. This will influence the power system scheduling and the dynamic control of wind turbine. The wind data used here is measured at 15 minute time intervals. The performance is evaluated based on the metrics, namely, mean square error, mean absolute error, sum squared error of the proposed model and compared with the back propagation model. Simulation studies are carried out and it is reported that the proposed model outperforms the compared model based on the metrics used and conclusions were drawn appropriately.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• 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.

• 한국지구과학회지
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• 제36권2호
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• pp.158-170
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• 2015

큰 에디 모의과정을 포함한 WRF 모델 (WRF-LES)을 이용하여 수치모델의 수평공간 규모에 따른 대기경계층 모수화 실험과 LES 모의 결과를 지표층 근처의 풍속 예측에 대하여 비교하였다. 수치실험은 복잡한 산악지형과 해안지역을 포함하는 강원도 지역에서 수평해상도 1 km와 333 m 실험을 수행하였다. 수평해상도 1 km 실험은 대기경계층 모수화 방안을 채택하였으며, 333 m 실험에서는 LES를 이용하였다. 복잡한 산악지역에서의 풍속 예측의 정확성은 수평해상도 1 km 실험 보다 333 m 실험에서 향상되었으며 해안지역에서는 1 km 실험에서 관측과 더 일치하였다. 지표층 근처의 큰 난류를 직접 계산하는 LES 실험은 산악지역의 풍속예측 개선에 기여하였다.

• 한국군사과학기술학회지
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• 제18권4호
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• pp.469-477
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• 2015

In this study, the characteristics of main wind direction, vertical temperature and wind speed profile near the Moseulpo airfield for HALE UAV(High Altitude Long Endurance Unmaned Aerial Vehicle) is investigated. The results are summarized as follows, main wind direction is governed by air mass according to season and local wind such as land-sea breeze. The directions of landing and take-off of HALE UAV will be selected as the south-east direction in June ~ August, north-west direction in October ~ March, and south-east direction at daytime in April ~ May, September. Annual variation of temperature at 100 hPa showed that temperature in summer season is lower than winter season. On the other hands, wind speed at 250 hPa in winter season is higher than summer season. The threshold values of temperature and wind speed for HALE UAV flight are $-75^{\circ}C$ and $90ms^{-1}$, which were determined by 5 % frequency value($1.96{\sigma}$), respectively.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.329-330
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• 2015

From the results of previous my paper [10] in 2015 year of economic and electrical power storage research conference in Naju, this paper describes an application of autoregressive and moving average (ARMA) model to forecast hourly average wind speed (HAWS) in Jeju island. The models are used to build up short-term forecast of hourly average wind speed by the weighted sum of previous wind speed values.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.433-438
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• 2020

Accurate forecasting of wind power is important for grid operation. Wind power has intermittent and nonlinear characteristics, which increases the uncertainty in wind power generation. In order to accurately predict wind power generation with high uncertainty, it is necessary to analyze the factors affecting wind power generation. In this paper, 6 factors out of 11 are selected for more accurate wind power generation forecast. These are wind speed, sine value of wind direction, cosine value of wind direction, local pressure, ground temperature, and history data of wind power generated.

• 대기
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• 제31권1호
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• pp.85-100
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• 2021

The necessity of accurate high-resolution meteorological forecasts becomes increasing in socio-economical applications and disaster risk management. The Korea Meteorological Administration Post-Processing (KMAPP) system has been operated to provide high-resolution meteorological forecasts of 100 m over the South Korea region. This study evaluates and improves the KMAPP performance in simulating wind speeds over complex terrain areas using the ICE-POP 2018 field campaign measurements. The mountainous measurements give a unique opportunity to evaluate the operational wind speed forecasts over the complex terrain area. The one-month wintertime forecasts revealed that the operational Local Data Assimilation and Prediction System (LDAPS) has systematic errors over the complex mountainous area, especially in deep valley areas, due to the orographic smoothing effect. The KMAPP reproduced the orographic height variation over the complex terrain area but failed to reduce the wind speed forecast errors of the LDAPS model. It even showed unreasonable values (~0.1 m s-1) for deep valley sites due to topographic overcorrection. The model's static parameters have been revised and applied to the KMAPP-Wind system, developed newly in this study, to represent the local topographic characteristics better over the region. Besides, sensitivity tests were conducted to investigate the effects of the model's physical correction methods. The KMAPP-Wind system showed better performance in predicting near-surface wind speed during the ICE-POP period than the original KMAPP version, reducing the forecast error by 21.2%. It suggests that a realistic representation of the topographic parameters is a prerequisite for the physical downscaling of near-ground wind speed over complex terrain areas.