## 4 Results and Discussion
### Development of Univariate Hybrid Model

Table 7: Performance results of different univariate WSP models for Southern Offshore TX region. The observation of the performance of the individual model for each time step of forecast horizons provides insights into the weight function that needs to be assigned to the individual model for each time step. Clearly, an individual model is not the best fit for all the time steps of the given forecast horizon. The \(t=1\) and \(t=2\) persistence model and ARIMA model work well for short-term wind speed prediction. The Weibull-based model is worse by a noticeable margin for this time step. However, the Weibull-based model is worse by a noticeable margin for this time step. However, the Weibull-based model is worse by a noticeable margin for this time step. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{RMSE} \\ \hline
**Models** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline PM & 2.23 & 2.23 & 2.23 & 2.23 & 2.23 & 2.23 & 2.23 \\ WEBSEC & 4.07 & 0.31 & 2.61 & 1.78 & 1.91 & 0.11 & 1.80 \\ ARIMA & 1.9 & 1.5 & 1.2 & 0.96 & 0.77 & 0.61 & 1.16 \\ SVR & 0.58 & 1.96 & 3.39 & 3.49 & 3.009 & 2.7 & 2.52 \\ LSTM & 0.22 & 0.19 & 0.16 & 0.14 & 0.13 & 0.12 & 0.16 \\ \hline \multicolumn{6}{c}{**MAE**} \\ \hline
**Models** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline PM & 0.99 & 2.29 & 2.73 & 2.6 & 2.35 & 2.34 & 2.22 \\ WEBSEC & 2.84 & 0.25 & 3.18 & 2.16 & 1.79 & 0.003 & 1.70 \\ ARIMA & 0.7 & 1.64 & 1.79 & 1.33 & 0.89 & 0.72 & 1.18 \\ SVR & 0.77 & 0.68 & 0.24 & 0.53 & 0.3 & 0.04 & 0.43 \\ LSTM & 1.007 & 0.25 & 0.72 & 0.51 & 0.25 & 0.23 & 0.49 \\ \hline \multicolumn{6}{c}{**MAPE**} \\ \hline
**Models** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline PM & 11.64 & 31.53 & 41.17 & 37.31 & 32.53 & 32.37 & 31.09 \\ WEBSEC & 33.16 & 3.46 & 46.89 & 30.99 & 24.84 & 0.05 & 23.23 \\ ARIMA & 8.2 & 22.5 & 26.4 & 19.1 & 12.3 & 10.007 & 16.42 \\ SVR & 8.9 & 9.4 & 3.6 & 7.6 & 4.1 & 0.6 & 5.70 \\ LSTM & 11.7 & 3.5 & 10.7 & 7.4 & 3.4 & 3.2 & 6.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance results of different univariate WSP models for West Virginia Hills region. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{RMSE} \\ \hline
**Models** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline PM & 0.79 & 0.79 & 0.79 & 0.79 & 0.79 & 0.79 & 0.79 \\ WEBSEC & 2.23 & 0.44 & 0.28 & 2.01 & 0.59 & 2.29 & 1.31 \\ ARIMA & 1.008 & 1.23 & 1.42 & 1.59 & 1.7 & 1.87 & 1.47 \\ SVR & 0.018 & 0.24 & 1.001 & 0.15 & 0.26 & 4.01 & 0.95 \\ LSTM & 2.08 & 2.05 & 2.03 & 2.06 & 2.02 & 2.01 & 2.04 \\ \hline \multicolumn{6}{c}{**MAE**} \\ \hline
**Models** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline PM & 0.22 & 0.89 & 0.421 & 0.08 & 3.81 & 2.38 & 1.30 \\ WEBSEC & 1.21 & 2.13 & 0.93 & 1.3 & 3.61 & 3.89 & 2.18 \\ ARIMA & 0.009 & 0.45 & 0.2 & 0.88 & 4.76 & 3.47 & 1.63 \\ SVR & 0.18 & 0.61 & 0.6 & 0.26 & 3.56 & 1.59 & 1.13 \\ LSTM & 3.1 & 3.73 & 3.24 & 2.73 & 0.99 & 0.41 & 2.37 \\ \hline \multicolumn{6}{c}{**MAPE**} \\ \hline
**Models** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline PM & 4.7 & 16.4 & 8.4 & 1.9 & 52.6 & 11.1 & 15.85 \\ WEBSEC & 25.5 & 39.2 & 18.7 & 29.3 & 49.9 & 18.1 & 30.12 \\ ARIMA & 0.2 & 8.3 & 4.2 & 20 & 65.7 & 16.19 & 19.10 \\ SVR & 3.8 & 11.3 & 12.2 & 6.05 & 49.2 & 74.3 & 26.14 \\ LSTM & 65.1 & 68.8 & 65.5 & 61.3 & 137.8 & 19.5 & 69.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance results of different univariate WSP models for Arizona Hills region. model holds steady performance as forecasting horizon projects into the future, while the performance of all other models deteriorates. The Weibull-based model is best for \(t=6\). The persistence model is worse when the time steps of forecast horizons increase.