## 5 Conclusions  
In this research, the performance of wind speed prediction models including persistence-forecasting-based models, classical time-series-based models, and machine-learning-based models for short-term forecast horizons was analyzed using four different case study sites. The results indicated the need for a hybrid wind speed prediction model that can incorporate all three types of wind speed prediction models to accurately predict wind speed for short-term forecast horizons. The proposed hybrid wind speed prediction was \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline RMSE & 0.9724 & 0.945 & 1.20825 & 1.3915 & 0.9315 & 2.368 & 1.302775 \\ MAE & 0.66385 & 1.27 & 1.21705 & 1.2315 & 3.392 & 2.8845 & 1.776483333 \\ MAPE & 13.99 & 23.42 & 24.635 & 27.7925 & 65.485 & 26.1035 & 30.23766667 \\ \hline \end{tabular}
\end{table} 
Table 13: Performance results of different univariate hybrid WSP models for Arizona Hills region. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline RMSE & 2.4385 & 2.2565 & 1.812 & 1.7275 & 1.0715 & 0.8665 & 1.695416667 \\ MAE & 0.8795 & 1.511 & 1.557 & 2.15 & 2.10565 & 1.6975 & 1.650108333 \\ MAPE & 9.38 & 18.651 & 21.7755 & 33.3205 & 35.776 & 31.3145 & 25.03625 \\ \hline \end{tabular}
\end{table} 
Table 12: Performance results of different univariate hybrid WSP models for West Virginia Hills region. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **Average** \\ \hline RMSE & 0.913 & 1.4775 & 1.2345 & 1.59 & 1.335 & 1.1225 & 1.27875 \\ MAE & 0.478 & 1.0877 & 0.557 & 1.4275 & 2.0095 & 2.492 & 1.34195 \\ MAPE & 4.7416 & 10.9635 & 4.4375 & 10.893 & 7.6055 & 8.429 & 7.845016667 \\ \hline \end{tabular}
\end{table} 
Table 10: Performance results of different univariate hybrid WSP models for South Plains TX region. developed using multivariate time series data, together with all of the other three models. This research highlights the competitive performance of the probability-density-based wind speed prediction method; the probability distribution of wind speed, which best describes the wind speed distribution of a given location, is also the best model for the wind speed prediction of the given location. Moreover, a novel method of error correction for wind speed forecasting based on the Weibull-distribution-based WSP model was proposed; this error correction can forecast wind speed accurately with MAPE of 4.7%, MAE of 1.7, and RMSE of 1, which is comparable to the best model out of the five models studied, i.e., MAPE of 5.7%, MAE of 0.43, and RMSE of 1.52. The wind speed of a region can be simulated based on the Weibull distribution parameters. After analyzing the model's performance in predicting each time step of the whole forecast horizon, a hybrid model based on univariate wind forecasting was developed by incorporating persistence, traditional time series, and machine learning. The weight function that needs to be allocated to each model for each time step was determined by observing how well each model performed for each time step of the forecast window. This study shows the competitive performance of the univariate model, which can be used where only univariate data are available for wind speed prediction. In this analysis, we suggested that the persistence model is the most accurate for predicting wind speed one or two hours in the future, a Weibull-based model for forecast horizons six hours or longer in the future, and an ARIMA or machine learning model is a good choice for forecast horizons in between two and six hours. As a result, a univariate model based on weight functions performs better and gives more weight to the most accurate approach for each time step. As this method used five different models for wind speed prediction, it might be more time-consuming when compared to a single model.