## 5 Conclusions  
In general, wind resources near the surface or sea level within the atmospheric boundary layer have very low predictability, because they induce characteristic changes in a short time by convection, temperature reversal layer, and wind shear caused by simultaneous forcing by mechanical and thermal factors. Accurately predicting wind power resources means accurately predicting wind power generation, which, in turn, can be a sure means of balancing electricity demand and supply with economic benefits for operators. In this study, in order to improve the prediction accuracy of wind power output, the concept of equivalent rotor wind speed recommended by IEC61400-12-1 was introduced to accurately reflect the wind speed characteristics of the rotor disk area. To determine how accurate and always accurate REWS compared to HHWS (Hub Altitude Wind Speed), two years of \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Stability Index**} & \multirow{2}{*}{**Turbine No.**} & \multicolumn{4}{c}{**Compared with REWS (Error Rate, \%)**} & \multicolumn{4}{c}{**Compared with HHWS (Error Rate, \%)**} \\ \cline{3-11}  & & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{4}{*}{Wind Shear} & Turbine A01 & 7.47 & \(-1.91\) & \(-0.96\) & \(-1.70\) & \(20.32\) & \(9.95\) & \(-0.14\) & \(0.41\) & \(0.19\) & \(26.01\) \\  & Turbine A02 & 10.17 & 0.44 & \(-0.12\) & \(-0.37\) & \(22.70\) & \(12.00\) & \(2.25\) & \(1.26\) & \(1.39\) & \(29.20\) \\  & Turbine A03 & 8.10 & \(-1.57\) & \(-1.07\) & \(-1.09\) & \(20.61\) & \(10.50\) & \(0.33\) & \(0.26\) & \(0.64\) & \(26.21\) \\  & Turbine B01 & 12.64 & 2.65 & 0.70 & 0.73 & 24.37 & 14.87 & 4.39 & \(1.95\) & \(2.77\) & \(29.50\) \\  & Turbine C01 & 12.86 & 0.35 & 1.22 & 1.62 & 24.98 & 15.00 & \(2.06\) & \(2.56\) & \(3.43\) & \(30.81\) \\ \hline \multirow{4}{*}{TI} & Turbine A01 & 78.99 & 34.26 & 3.52 & \(-2.70\) & \(-4.16\) & \(80.47\) & \(37.89\) & \(6.27\) & \(-0.40\) & \(-3.00\) \\  & Turbine A02 & 78.40 & 37.32 & 6.10 & \(-0.79\) & \(-2.78\) & \(79.32\) & \(40.35\) & \(8.79\) & \(1.51\) & \(-1.72\) \\  & Turbine A03 & 79.40 & 34.44 & 3.60 & \(-2.36\) & \(-3.99\) & \(80.26\) & \(38.36\) & \(6.44\) & \(-0.16\) & \(-2.86\) \\  & Turbine B01 & 80.31 & 43.11 & 10.92 & 0.58 & \(-2.34\) & \(80.70\) & \(46.57\) & \(13.71\) & \(2.80\) & \(-1.29\) \\  & Turbine C01 & 81.89 & 39.88 & 8.39 & \(-0.42\) & \(-1.90\) & \(83.75\) & \(42.86\) & \(11.28\) & \(1.71\) & \(-0.72\) \\ \hline \multirow{4}{*}{Richardson Number} & Turbine A01 & 2.55 & \(-2.33\) & \(-0.88\) & \(-2.88\) & \(-3.53\) & \(6.59\) & \(-0.86\) & \(0.20\) & \(-1.50\) & \(-2.01\) \\  & Turbine A02 & 6.20 & \(-1.68\) & \(-0.82\) & \(-2.35\) & \(-2.54\) & \(8.29\) & \(-0.17\) & \(0.53\) & \(-1.09\) & \(-0.18\) \\  & Turbine A03 & 2.27 & \(-2.24\) & \(-1.12\) & \(-3.36\) & \(-4.49\) & \(4.67\) & \(-0.81\) & \(-0.06\) & \(-2.34\) & \(-1.99\) \\  & Turbine B01 & 9.89 & \(-0.14\) & \(-0.42\) & \(-2.76\) & \(-0.91\) & \(12.31\) & \(1.06\) & \(0.61\) & \(-1.40\) & \(1.62\) \\  & Turbine C01 & 6.45 & 0.04 & \(-0.93\) & \(-2.08\) & \(-1.33\) & \(8.19\) & \(1.55\) & \(-0.06\) & \(-0.77\) & \(0.61\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Error rate between the simulated power output based on REWS and HHWS, and the actual power output by atmospheric stability classification method (Wind shear, TI, Richardson Number). SCADA data were analyzed and validated at the Anholt Offshore Wind Farm in Denmark. After filtering to secure valid data that satisfies the purpose of the study, 41.6% of the final data was analyzed. REWS- and HHWS-based simulated power output have different error rates than actual power output over each time. From midnight to 14:00, the error rate (\(-\)0.11%) between REWS-based simulated power output and actual power output tended to decrease compared to the error rate of HHWS (1.56%). The difference in accuracy of each method according to time change is directly related to the atmospheric stability.