## 6 Conclusions and Future Work  
The accuracy of wind speed forecasting is crucial for the reliability of wind energy. In this work, we study a Bayesian clustering-based deep learning model for short-term wind speed forecasting to improve the prediction accuracy. The proposed model takes advantage of clustering the features obtained from the LSTM layer. Features extracted from the LSTM layer will reveal the temporal relationship of the time series data, which will  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models-Metrics**} & \multicolumn{3}{c}{**Amasra**} & \multicolumn{3}{c}{**Pinarbasi**} & \multicolumn{3}{c}{**Cesme**} & \multicolumn{3}{c}{**Gokceada**} \\ \cline{2-13}  & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** \\ \hline _Golabul LSTM_ & 0.186 & 8.686 & 0.269 & 0.231 & 6.959 & 0.358 & 0.172 & 4.420 & 0.260 & 0.197 & 4.590 & 0.311 \\ _DP-Local LSTM_ & 0.185 & 8.607 & 0.273 & 0.225 & 6.499 & 0.354 & 0.166 & 4.120 & 0.260 & 0.199 & 4.957 & 0.326 \\ _DTW-Local LSTM_ & 0.193 & 8.801 & 0.283 & 0.237 & 6.889 & 0.366 & 0.169 & 4.085 & 0.262 & 0.196 & 4.596 & 0.325 \\ _DP-ensemble model_ & 0.175 & 8.266 & 0.258 & 0.217 & 6.417 & 0.343 & 0.161 & 4.090 & 0.250 & 0.186 & 4.539 & 0.304 \\ _DTW-ensemble model_ & 0.179 & 8.347 & 0.262 & 0.222 & 6.564 & 0.347 & 0.164 & 4.114 & 0.253 & 0.187 & 4.418 & 0.305 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of each method for a different test location.  
Figure 7: Actual and forecasted wind speed values for each method for 24 h time horizon.  
make the segmentation of the time series more accurate. Particularly, Dirichlet mixture modeling and dynamic time warping methods are applied to features extracted at the LSTM layer to cluster time series data to focus on short-term patterns. The ensemble model captures both long and short-term patterns integrating global and local models.  
The numerical study with four different data shows that the proposed ensemble model improves the prediction accuracy based on three evaluation metrics (MAE, MAPE, RMSE). Our results also show that all of the methods studied achieve relatively good performance in wind forecasting. The ensemble models' prediction accuracy are the best among studied methods regardless of the wind regime and meteorological aspects (for all locations). Our results indicate that the proposed models will improve wind energy investments' reliability and economic success. The numerical results also reveal that clustering approaches have critical roles in prediction accuracy.  
The proposed deep learning model is trained on large data (with 141,984 data points), which is computationally demanding. In future work, instead of training the whole model, transfer learning method can be used to extract features. In particular, features could be obtained from a pretrained model, and train only the last layer to save substantial computational resources. It might also be worthwhile to use different nonparametric methods for clustering features. Models with different data types from different areas (such as marketing and supply chain management) might be another important extension. Another possible extension of this study would be utilizing different machine learning algorithms to the proposed approach.  
This research received no external funding.