• Journal of the Korean Solar Energy Society
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• v.31 no.6
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• pp.8-22
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• 2011

Investigations on modeling methods of a CFD wind resource prediction program, WindSim for a ccurate predictions of wind speeds were performed with the field measurements. Meteorological Masts having heights of 40m and 50m were installed at two different sites in complex terrain. The wind speeds and direction were monitored from sensors installed on the masts and recorded for one year. Modeling parameters of WindSim input variables for accurate predictions of wind speeds were investigated by performing cross predictions of wind speeds at the masts using the measured data. Four parameters that most affect the wind speed prediction in WindSim including the size of a topographical map, cell sizes in x and y direction, height distribution factors, and the roughness lengths were studied to find out more suitable input parameters for better wind speed predictions. The parameters were then applied to WindSim to predict the wind speed of another location in complex terrain in Korea for validation. The predicted annual wind speeds were compared with the averaged measured data for one year from meteorological masts installed for this study, and the errors were within 6.9%. The results of the proposed practical study are believed to be very useful to give guidelines to wind engineers for more accurate prediction results and time-saving in predicting wind speed of complex terrain that will be used to predict annual energy production of a virtual wind farm in complex terrain.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.1-10
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• 2012

The annual energy production of Gangwon wind farm was predicted for three consecutive years of 2007, 2008 and 2009 using commercial programs, WindPRO and WindSim which are known to be used the most for wind resource prediction in the world. The predictions from the linear code, WindPRO, were compared with both the actual energy prediction presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm and also the predictions from the CFD code, WindSim. The results from WindPRO were close to the actual energy productions and the errors were within 11.8% unlike the expectation. The reason for the low prediction errors was found to be due to the fact that although the wind farm is located in highly complex terrain, the terrain steepness was smaller than a critical angle($21.8^{\circ}$) in front of the wind farm in the main wind direction. Therefore no flow separation was found to occur within the wind farm. The flow separation of the main wind was found to occur mostly behind the wind farm.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.59-70
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• 2023

In order to predict low-level wind shear at Incheon International Airport (RKSI), a Low-Level Wind Shear prediction system (KMAP-LLWS) along the runway take-off and landing route at RKSI was established using Korea Meteorological Administration Post-Processing (KMAP). For the performance evaluation, the case of low-level wind shear cases calculated from Aircraft Meteorological Data Relay (AMDAR) from July 2021 to June 2022 was used. As a result of verification using the performance evaluation index, POD, FAR, CSI, and TSS were 0.5, 0.85, 0.13, and 0.34, respectively, and the prediction performance was improved by POD, CSI, and TSS compared to the Low-Level Wind Shear prediction system (LDPS-LLWS) calculated using the Korea Meteorological Administration's Local Data Assimilation and Prediction System (LDAPS). This means that the use of high-resolution numerical models improves the predictability of wind changes. In addition, to improve the high FAR of KMAP-LLWS, the threshold for low-level wind shear strength was adjusted. As a result, the most effective low-level wind shear threshold at 8.5 knot/100 ft was derived. This study suggests that it is possible to predict and respond to low-level wind shear at RKSI. In addition, it will be possible to predict low-level wind shear at other airports without wind shear observation equipment by applying the KMAP-LLWS.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.1
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• pp.86-93
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• 2015

Globally, wind power development is experiencing dramatic growth and wind power penetration levels are increasing. Wind generation is highly variable in time and space and it doesn't guarantee the system reliability and secure system operation. As wind power capacity becomes a significant portion of total generation capacity, the reliability assessment for wind power are therefore needed. At present, this operational reliability assessment is focusing on a generation adequacy perspective and does not consider transmission reliability issues. In this paper, we propose the critical transmission operating constraint prediction(CTOCP) system with high wind power penetration to enhance transmission reliability.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.627-630
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• 2016

Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

• Journal of Power System Engineering
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• v.3 no.1
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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 Power System Engineering
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• v.21 no.6
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• pp.31-39
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• 2017

In order to examine if met-masts wind data can exchange each other for wind resource assessment, an investigation was carried out in Kimnyeong and Haengwon regions of Jeju Island. The two regions are both simple terrain and 4.31 km away from each other. The one-year wind speed data measured by 70 m-high anemometers of each met-mast of the two regions were analysed in detail. Measure-Correlate-Predict (MCP) method was applied to the two regions using the 10-year Automatic Weather System (AWS) wind data of Gujwa region for creating 10-year Wind Statistics by running WindPRO software. The two 10-year Wind Statistics were applied to the self-met mast point for self prediction of Annual Energy Production (AEP) and Capacity Factor (CF) and the each other's met mast point for mutual prediction of them. As a result, when self-prediction values were reference, relative errors of mutual prediction values were less than 1% for AEP and CF so that met masts wind data under the same condition of this study could exchange each other for estimating accurate wind resource.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.381-394
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• 2020

Accelerated soil wind erosion still remains to date to cause severe economic and environmental impacts. Revised and updated models to quantitatively evaluate wind induced soil erosion have been made for specific factors in the wind erosion equation (WEQ) framework. Because of increasing quantities of accumulated data, the WEQ, the revised wind erosion equation (RWEQ), the wind erosion prediction system (WEPS), and other soil wind erosion models have been established. These soil wind erosion models provide essential knowledge about where and when wind erosion occurs although naturally, they are less accurate than the field-scale. The WEQ was a good empirical model for comparing the effects of various management practices on potential erosion before the RWEQ and the WEPS showed more realistic estimates of erosion using easily measured local soil and climatic variables as inputs. The significant relationship between the observed and predicted transport capacity and soil loss makes the RWEQ a suitable tool for a large scale prediction of the wind erosion potential. WEPS developed to replace the empirical WEQ can calculate soil loss on a daily basis, provide capability to handle nonuniform areas, and obtain predictions for specific areas of interest. However, the challenge of precisely estimating wind erosion at a specific regional scale still remains to date.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.107-112
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• 2015

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing.