• 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.

• 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.

• 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.

• 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.

• Journal of the Korean Solar Energy Society
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• v.33 no.5
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• pp.1-8
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• 2013

Wind resource data of short-term period has to be corrected a long-term period by using MCP method that Is a statistical method to predict the long-term wind resource at target site data with a reference site data. Because the field measurement for wind assessment is limited to a short period by various constraints. In this study, 2 different MCP methods such as Linear regression and Matrix method were chosen to compare the predictive accuracy between the methods. Finally long-term wind speed, wind power density and capacity factor at the target site for 20 years were estimated for the variability of wind and wind energy. As a result, for 20 years annual average wind speed, Yellow sea off shore wind farm was estimated to have 4.29% for coefficient of variation, CV, and -9.57%~9.53% for range of variation, RV. It was predicted that the annual wind speed at Yellow sea offshore wind farm varied within ${\pm}10%$.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.262-267
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• 2008

The long-term wind data are reconstructed from the short-term meteorological data to design the 4 MW offshore wind park which will be constructed at Woljeong-ri, Jeju island, Korea. Using two MCP (Measure-Correlate-Predict) models, the relative deviation of wind speed and direction from two neighboring reference weather stations can be regressed at each azimuth sector. The validation of the present method is checked about linear and matrix MCP models for the sets of measured data, and the characteristic wind turbulence is estimated from the ninety-percent percentile of standard deviation in the probability distribution. Using the Gumbel's model, the extreme wind speed of fifty-year return period is predicted by the reconstructed long-term data. The predicted results of this analysis concerning turbulence intensity and extreme wind speed are used for the calculation of fatigue life and extreme load in the design procedure of wind turbine structures at offshore wind farms.

• 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.

• New & Renewable Energy
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• v.18 no.1
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• pp.17-28
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• 2022

This paper presents an effective Markov transition matrix (EMTM), which will be used to calculate the wind speed at the target site in a wind farm to accurately predict wind energy production. The existing MTS prediction method using a Markov transition matrix (MTM) exhibits a limitation where significant prediction variations are observed owing to random selection errors and its bin width. The proposed method selects the effective states of the MTM and refines its bin width to reduce the error of random selection during a gap filling procedure in MTS. The EMTM reduces the level of variation in the repeated prediction of wind speed by using the coefficient of variations and range of variations. In a case study, MTS exhibited better performance than other MCP models when EMTM was applied to estimate a one-day wind speed, by using mean relative and root mean square errors.

• 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.