• Journal of Wind Energy
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• v.9 no.4
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• pp.9-15
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• 2018

In a previous study, a technique for measuring wind speed and direction by using a roll-rotating three-axis ultrasonic anemometer was proposed and verified by wind tunnel tests. In the tests, instead of a roll sensor, roll angle was trimmed to make no up flow in the transformed wind speeds. Verification was done in point of the residual error of the rotation effect treatment. In this study, roll angle was measured from the roll motor encoder and the transformed wind speed and direction on the test section axis were compared with the ones provided to the test section. As a result, up to yaw $20^{\circ}$ at a wind speed of 12 m/sec or over, the RMS error of wind speed was within the double of the ultrasonic anemometer error. But at yaw $30^{\circ}$, it was over the double of the ultrasonic anemometer error. Regardless of wind speed, at yaw $20^{\circ}$ and $30^{\circ}$, the direction error was within the double of the ultrasonic anemometer error. But at yaw $10^{\circ}$ or less, it was within the error of the ultrasonic anemometer itself. This is a very favorable characteristic to be used for wind turbine yaw control.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.199-207
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• 2008

A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.268-273
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• 2008

A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• Wind and Structures
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• v.18 no.6
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• pp.619-631
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• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

• Wind and Structures
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• v.32 no.2
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• pp.161-167
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• 2021

In this study, the artificial neural network (ANN) method was used for estimating the monthly mean wind speed of Sivas, in the central part of Turkey. Eighteen years of wind speed data obtained from nine measurement stations during the period of 2000-2017 at 10 m height was used for ANN analysis. It was found that mean absolute percentage error (MAPE) ranged from 3.928 to 6.662, mean bias error (MBE) ranged from -0.089 to -0.003, while root mean square error (RMSE) ranged from 0.050 to 0.157 and R2 ranged from 0.86 to 0.966. ANN models provide a good approximation of the wind speed for all measurement stations, however, a tendency to underestimate is also obvious.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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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 the Korean Solar Energy Society
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• v.31 no.3
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• pp.1-7
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• 2011

In the performance test for wind turbines of medium and large, The reference met-mast should be installed for measurement reference wind speed as IEC 61400-12-1 standards and design of booms for mounted an anemometer must be considered exactly. Boom-mounted cup anemometer are influenced by flow distortion of the mast and the boom. Therefore design of booms must be important so that flow distortion due to booms should be kept below 0.5%. But, in some cases at size of met-mast structure, the distance of boom from mast is longer then measurement of wind speed is impossible because of oscillation of boom-mounted anemometer. In this paper, We measured a wind speed at several point from mast and boom and we analyzed the error of wind speed at each point of measurement. Also, we will suggest a correction method using the data curve fitting about errors of wind speed between each point of mounted anemometer.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.326-332
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• 2012

In this paper, error analyses on the calculation of offshore wind speed have been conducted using HeMOSU-1 data to develop offshore wind energy in Yeonggwang sea of Korea and onshore observed wind data in Buan, Gochang and Yeonggwang for 2011. Offshore wind speed data at 98.69 m height above M.S.L is estimated using relational expression induced by linear regression analysis between onshore and offshore wind data. In addition, estimated offshore wind speed data is set at 87.65 m above M.S.L using power law wind profile model with power law exponent(0.115) and its results are compared with the observed data. As a result, the spatial adjustment error are 1.6~2.2 m/s and the altitude adjustment error is approximately 0.1 m/s. This study shows that the altitude adjustment error is about 5% of the spatial adjustment error. Thus, long term observed data are needed when offshore wind speed was estimated by onshore wind speed data. because the conversion of onshore wind data lead to large error.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.130-139
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• 2017

The crop damage caused by strong wind was predicted using the wind speed data available from Korean Meteorological Administration (KMA). Wind speed data measured at 19 automatic weather stations in 2012 were compared with wind data available from the KMA's digital forecast. Linear regression equations were derived using the maximum value of wind speed measurements for the three-hour period prior to a given hour and the digital forecasts at the three-hour interval. Estimates of daily maximum wind speed were obtained from the regression equation finding the greatest value among the maximum wind speed at the three-hour interval. The estimation error for the daily maximum wind speed was expressed using normal distribution and Weibull distribution probability density function. The daily maximum wind speed was compared with the critical wind speed that could cause crop damage to determine the level of stages for wind damage, e.g., "watch" or "warning." Spatial interpolation of the regression coefficient for the maximum wind speed, the standard deviation of the estimation error at the automated weather stations, the parameters of Weibull distribution was performed. These interpolated values at the four synoptic weather stations including Suncheon, Namwon, Imsil, and Jangsu were used to estimate the daily maximum wind speed in 2012. The wind damage risk was determined using the critical wind speed of 10m/s under the assumption that the fruit of a pear variety Mansamgil would begin to drop at 10 m/s. The results indicated that the Weibull distribution was more effective than the normal distribution for the estimation error probability distribution for assessing wind damage risk.