• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.17-20
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• 2005

This paper presents an overview analysis on the observed wind shear at Pohang Steel Works, focusing on diurnal patterns and the frequency of high nighttime shear at the site in case of land breeze. In addition, this paper discusses the importance of accurate shear estimates for reliable evaluation of wind energy density. In order for a long-term correlation of the site, three Measure-Correlate-Predict methods were tested with Pohang wind data and it was shown that the linear MCP gives poor estimation due to the geographic characteristics of complex terrain where the severe transformation of wind direction was accompanied.

• New & Renewable Energy
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• v.1 no.2 s.2
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• pp.26-33
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• 2005

This paper presents and overview analysis on the observed wind shear at Pohang Steel Works. focusing on diurnal patterns and the frequency of high nighttime shear at the site in case of land breeze. In addition, this paper discusses the importance of accurate shear estimation for reliable evaluation of wind energy density. In order for long-term correlation of the site, three Measure-Correlate-Predict methods were tested with Pohang wind data and it was shown that the linear MCP gives poor estimation due to the topological characteristics of complex terrain where the severe transformation of wind direction was accompanied.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.278-281
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• 2008

In these days the installation of wind turbines or wind parks includes a high financial risk. So for the planning and the constructing of wind farms, long-term data of wind speed and wind direction is required. However, in most cases only few data are available at the designated places. Traditional Measure-Correlate-Predict (MCP) can extend this data by using data of nearby meteorological stations. But also Neural Networks can create such long-term predictions. The key issue of this paper is to demonstrate the possibility and the quality of predictions using Neural Networks. Thereto this paper compares the results of different MCP Models and Neural Networks for creating long-term data with various indexes.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.37-43
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• 2012

Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• Journal of the Korean Solar Energy Society
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• v.26 no.2
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• pp.27-34
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• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

• New & Renewable Energy
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• v.4 no.4
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.323-324
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• 2006

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3A
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• pp.209-216
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• 2009

The structural stability as well as economical efficiency of the wind sensitive structures are strongly dependant on accurate evaluation of the design wind speed. Present study demonstrates a useful wind data obtained at the wind monitoring tower in the Kwangyang Suspension Bridge site. Moreover the Measure-Correlate-Predict (MCP) method has been applied to estimate the long-term wind data at the bridge site based on the wind data at the local weather station. The measured data indicate that the turbulent intensities and roughness exponents are strongly affected by the wind direction and surrounding topography. The new design wind speed based on MCP method is 20m/s lower than that at the original estimation, and the resulting design wind load is only 36% of the old prediction. The field measurement of wind data is recommended to ensure the economical and secure design of the wind sensitive structures because the measured wind data reveal much different from the estimated one due to local topography.