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Error Analysis of Measure-Correlate-Predict Methods for Long-Term Correction of Wind Data
Franz Vaas,Hyun-Goo Kim,Hyun-Soo Seo,Seok-Woo Kim 한국신재생에너지학회 2008 한국신재생에너지학회 학술대회논문집 Vol.2008 No.10
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.
Application of Neural Network for Long-Term Correction of Wind Data
김현구(Kim, Hyun-Goo),Vaas, Franz 한국신재생에너지학회 2008 신재생에너지 Vol.4 No.4
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.
서현수(Hyunsoo Seo),경남호(Nam-Ho Kyong),Franz Vaas,김현구(Hyun-Goo Kim) 한국신재생에너지학회 2008 한국신재생에너지학회 학술대회논문집 Vol.2008 No.10
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.