# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 4 Summary and Conclusions  
Achieving accurate wind power forecasting helps to specify scientific energy production plans in advance, which can help us to avoid supply and demand imbalance. To enable the trained LSTM network model to overcome the effect of noise, the SG filter was used to process the output of the training set. Through test comparisons, it can be found that proper filtering parameters help to allow the model to learn data features better, thus showing better generalization performance in the test. The main findings are summarized as follows:  
1. For the second-order SG filter, a data frame length setting of 5 is the best solution for signal quality improvement. 2. Optimization of the data used for learning allowed the RMSE of single-step prediction to decrease from 247.4 to 115.52, i.e., a 3.8% reduction in error over the range of values \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Error** & **10 Min** & **20 Min** & **30 Min** & **40 Min** & **50 Min** & **60 Min** \\ \hline \multirow{3}{*}{Linear Regression} & nRMSE & 0.0614 & 0.0856 & 0.1014 & 0.1133 & 0.1236 & 0.1326 \\  & nMAE & 0.0358 & 0.0523 & 0.0632 & 0.0722 & 0.08 & 0.0868 \\  & \(R^{2}\) & 0.9727 & 0.9469 & 0.9255 & 0.9071 & 0.8894 & 0.8728 \\ \hline \multirow{3}{*}{K- Neighbors} & nRMSE & 0.0789 & 0.1038 & 0.122 & 0.1365 & 0.1498 & 0.1607 \\  & nMAE & 0.0469 & 0.0629 & 0.0746 & 0.0839 & 0.0928 & 0.1002 \\  & \(R^{2}\) & 0.9550 & 0.9220 & 0.8922 & 0.8652 & 0.8375 & 0.8131 \\ \hline \multirow{3}{*}{Ridge} & nRMSE & 0.0614 & 0.0856 & 0.1014 & 0.1133 & 0.1236 & 0.1325 \\  & nMAE & 0.0359 & 0.0523 & 0.0632 & 0.0722 & 0.0800 & 0.0868 \\  & \(R^{2}\) & 0.9727 & 0.9469 & 0.9255 & 0.9071 & 0.8894 & 0.8728 \\ \hline \multirow{3}{*}{Random Forest} & nRMSE & 0.0667 & 0.0924 & 0.1103 & 0.1239 & 0.1336 & 0.1445 \\  & nMAE & 0.0395 & 0.0571 & 0.0700 & 0.0798 & 0.0872 & 0.0958 \\  & \(R^{2}\) & 0.9678 & 0.9382 & 0.9119 & 0.8888 & 0.8709 & 0.8489 \\ \hline \multirow{3}{*}{xgBoost} & nRMSE & 0.0672 & 0.0946 & 0.1108 & 0.1245 & 0.1341 & 0.1457 \\  & nMAE & 0.0385 & 0.0557 & 0.0669 & 0.0765 & 0.0832 & 0.0914 \\  & \(R^{2}\) & 0.9673 & 0.9352 & 0.9111 & 0.8878 & 0.8699 & 0.8464 \\ \hline \multirow{3}{*}{Adaboost} & nRMSE & 0.1545 & 0.1897 & 0.1963 & 0.2015 & 0.1977 & 0.1898 \\  & nMAE & 0.1320 & 0.1593 & 0.1638 & 0.1713 & 0.1648 & 0.1602 \\  & \(R^{2}\) & 0.8272 & 0.7396 & 0.7211 & 0.7062 & 0.7171 & 0.7393 \\ \hline \multirow{3}{*}{SVR} & nRMSE & 0.0767 & 0.0981 & 0.1134 & 0.1245 & 0.134 & 0.1426 \\  & nMAE & 0.0606 & 0.0754 & 0.0861 & 0.0934 & 0.0997 & 0.1057 \\  & \(R^{2}\) & 0.9574 & 0.9303 & 0.9069 & 0.8878 & 0.87 & 0.8528 \\ \hline \multirow{3}{*}{MLP} & nRMSE & 0.0631 & 0.0905 & 0.1033 & 0.1225 & 0.1260 & 0.1350 \\  & nMAE & 0.0378 & 0.0586 & 0.0633 & 0.0839 & 0.0790 & 0.0886 \\  & \(R^{2}\) & 0.9712 & 0.9408 & 0.9228 & 0.8913 & 0.8850 & 0.8680 \\ \hline \multirow{3}{*}{RNN} & nRMSE & 0.0719 & 0.1037 & 0.1178 & 0.1179 & 0.1306 & 0.1499 \\  & nMAE & 0.0466 & 0.0713 & 0.0843 & 0.0797 & 0.0901 & 0.1142 \\  & \(R^{2}\) & 0.9626 & 0.9222 & 0.8995 & 0.8994 & 0.8766 & 0.8373 \\ \hline \multirow{3}{*}{LSTM} & nRMSE & 0.0636 & 0.0878 & 0.1031 & 0.1154 & 0.1267 & 0.1366 \\  & nMAE & 0.0413 & 0.0561 & 0.0633 & 0.0741 & 0.0799 & 0.0853 \\  & \(R^{2}\) & 0.9707 & 0.9442 & 0.9231 & 0.9037 & 0.8839 & 0.8650 \\ \hline \multirow{3}{*}{Seq2 Seq} & nRMSE & 0.1578 & 0.1706 & 0.1238 & 0.1426 & 0.1586 & 0.1608 \\  & nMAE & 0.1208 & 0.1288 & 0.0854 & 0.0894 & 0.1161 & 0.1226 \\  & \(R^{2}\) & 0.8198 & 0.7894 & 0.8891 & 0.8527 & 0.8179 & 0.8128 \\ \hline \multirow{3}{*}{SG-LSTM} & nRMSE & **0.0485** & **0.0772** & **0.0932** & **0.1092** & **0.1168** & **0.1299** \\  & nMAE & **0.0297** & **0.0482** & **0.0578** & **0.0741** & **0.0751** & **0.0872** \\ \cline{1-1}  & \(R^{2}\) & **0.9828** & **0.9563** & **0.9363** & **0.9126** & **0.9082** & **0.8763** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different models for forward multi-step power prediction. (0-3500 kW).