# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 5 Conclusions  
This study presented a TCN-based model for day-ahead wind power prediction based on a casual convolution architecture with residual connections, in order to learn correlations between meteorological features and wind power generation. The proposed scheme effectively solves the long-distance dependency problem, as demonstrated by the input of large amounts of temporal-spatial series data such as one-year wind power data. The experimental results indicate that TCN models have the capability for feature extraction of long-term sequence data, and exhibit the same or higher prediction accuracy compared to LSTM and GRU models. Overall, the proposed TCN-based approach provides a lower convergence error with higher prediction accuracy than those of other models employed in other studies of wind power forecasting [12; 13; 31].  
**Author Contributions:** Conceptualization, P.W. and K.-M.C.; methodology, W.-H.L.; resources, P.W.; formal analysis, W.-H.L.; data curation, W.-H.L.; writing--original draft, Z.-Y.Y., Y.-H.L. and W.-H.L.; writing-review and editing, P.W.; software, H.-C.L. and Y.-H.L.; validation, H.-C.L. and W.-H.L.; visualization, Y.-H.L. and Z.-Y.Y.; project administration, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period Performance** & **Excellent** & **Good** & **Medium** & **Bad** \\ \hline Week & RNN & GRU & LSTM & TCN \\ \hline Month & LSTM & GRU & TCN & RNN \\ \hline Year & TCN & LSTM & GRU & RNN \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison of four DNN models for wind power forecasting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Team** & **Prediction Cycle** & **Prediction Intervals** &
\begin{tabular}{c} **Prediction Error** \\ **(MAPE)** \\ \end{tabular} \\ \hline ANEMOS, & & & \\ ANEMOS.plus \& & Short-term & within 36 h & 17–35\% \\ SafeWind, 2011 [31] & & & \\ \hline ANEMOS.plus \& & & \\ SafeWind, 2017 [12] & Short-term & within 48 h & 11–14\% \\ \hline UK Power Networks & & & \\
2016 [13] & Medium-term & within 120 h & 10\% \\ \hline Proposed model & Medium-term & within 72 h & Near 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance comparison of other real projects for wind power forecasting.  
**Funding:** This research was funded by the Ministry of Science and Technology of Taiwan under Grant Nos. MOST 110-2410-H-168-003, and Taiwan's Ministry of Education (MOE) under Grant No. MOE 2000-109 CC5-001.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** This work was jointly supported by Taiwan's Ministry of Science and Technology under Grant No. MOST 110-2410-H-168-003 and Taiwan's Ministry of Education (MOE) under Grant No. MOE 2000-109 CC5-001.  
**Conflicts of Interest:** The authors declare no conflict of interest