# Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
## 5 Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Step** & **Algorithms** & **MAE/MW** & **MAPE/\%** & **RMSE/MW** \\ \hline \multirow{8}{*}{\(1\)} & DWT\_LSTM & 11.26 & 2.02 & 17.35 \\  & DWT\_RNN & 12.16 & 2.60 & 17.57 \\  & DWT\_BP & 14.27 & 2.79 & 22.51 \\  & LSTM & 28.78 & 5.07 & 46.08 \\  & RNN & 29.97 & 5.17 & 46.20 \\  & BP & 30.18 & 5.19 & 46.11 \\ \hline \multirow{8}{*}{\(2\)} & DWT\_LSTM & 21.11 & 3.52 & 32.13 \\  & DWT\_RNN & 24.84 & 3.95 & 34.26 \\  & DWT\_BP & 25.23 & 4.68 & 38.28 \\  & LSTM & 36.71 & 6.56 & 55.32 \\  & RNN & 37.23 & 6.79 & 56.02 \\  & BP & 37.86 & 6.91 & 56.52 \\ \hline \multirow{8}{*}{\(3\)} & DWT\_LSTM & 28.55 & 5.31 & 43.6 \\  & DWT\_RNN & 29.64 & 5.40 & 44.09 \\  & DWT\_BP & 35.97 & 6.74 & 52.18 \\  & LSTM & 43.70 & 8.05 & 64.87 \\  & RNN & 44.75 & 8.16 & 66.05 \\  & BP & 47.27 & 8.32 & 68.35 \\ \hline \multirow{8}{*}{\(4\)} & DWT\_LSTM & 38.13 & 6.96 & 55.91 \\  & DWT\_RNN & 39.24 & 7.17 & 56.34 \\  & DWT\_BP & 46.68 & 8,12 & 67.65 \\  & LSTM & 48.91 & 9.02 & 72.88 \\  & RNN & 51.83 & 9.33 & 74.65 \\  & BP & 52.00 & 9.78 & 75.16 \\ \hline \multirow{8}{*}{\(5\)} & DWT\_LSTM & 44.52 & 8.36 & 64.62 \\  & DWT\_RNN & 44.73 & 8.77 & 65.21 \\  & DWT\_BP & 48.10 & 9.08 & 70.99 \\ \cline{1-1}  & LSTM & 55.71 & 10.17 & 81.56 \\ \cline{1-1}  & RNN & 58.29 & 10.21 & 83.33 \\ \cline{1-1}  & BP & 59.78 & 10.59 & 84.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation indexes of farm 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Step** & **Algorithms** & **MAE/MW** & **MAPE/\%** & **RMSE/MW** \\ \hline \multirow{8}{*}{\(1\)} & DWT\_LSTM & 5.49 & 1.75 & 8.64 \\  & DWT\_RNN & 5.63 & 1.89 & 8.67 \\  & DWT\_BP & 8.96 & 3.64 & 12.95 \\  & LSTM & 15.23 & 4.41 & 24.13 \\  & RNN & 15.28 & 4.41 & 24.16 \\  & BP & 15.74 & 4.94 & 24.33 \\ \hline \multirow{8}{*}{\(2\)} & DWT\_LSTM & 10.21 & 3.04 & 15.75 \\  & DWT\_RNN & 10.40 & 3.11 & 15.96 \\  & DWT\_BP & 13.12 & 3.95 & 20.29 \\  & LSTM & 18.36 & 5.43 & 28.62 \\  & RNN & 18.96 & 5.48 & 29.41 \\  & BP & 19.77 & 6.87 & 29.44 \\ \hline \multirow{8}{*}{\(3\)} & DWT\_LSTM & 15.27 & 4.35 & 22.92 \\  & DWT\_RNN & 15.82 & 5.36 & 23.46 \\ \cline{1-1}  & DWT\_BP & 19.71 & 7.66 & 28.15 \\ \cline{1-1}  & LSTM & 22.80 & 6.65 & 34.95 \\ \cline{1-1}  & RNN & 23.35 & 6.67 & 35.17 \\ \cline{1-1}  & BP & 23.92 & 8.42 & 35.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation indexes of farm 3. In the Equations (18)-(20), \(y\_real_{i}\) and \(y\_pred_{i}\) represent the real power generation and predicted power generation at time \(i\), respectively. Take Table 1 for example--the analysis of evaluation index is as follows:  
(1) For any prediction step size, MAE, MAPE, and RMSE of DWT_LSTM were lower than DWT_RNN, DWT_BP, LSTM, RNN, and BP, so the prediction performance of DWT_LSTM proposed in this paper is the best. Taking the experimental results of one step as an example, the MAE of DWT_LSTM was sequentially reduced by 27.46%, 38.37%, 64.25%, 65.22%, and 68.63%, respectively, compared with DWT_RNN, DWT_BP, LSTM, RNN, and BP; the MAPE of DWT_RNN, DWT_BP, LSTM, RNN, and BP was sequentially reduced by 11.73%, 37.94%, 37.29%, 41.78%, and 45.57%, the RMSE of DWT_RNN, DWT_BP, LSTM, RNN, and BP was sequentially reduced by 33.01%, 37.63%, 63.80%, 64.90%, and 65.87%. It can be seen that compared with the other five prediction methods, the prediction accuracy of DWT_LSTM was significantly improved, which indicates that the application of discrete wavelet transform enables independent LSTMs to more fully dig the major information and minor information contained in the approximate signal and the detail signal, respectively. Obviously, the discrete wavelet transform improves the prediction accuracy of wind power indeed. (2) For any prediction step size, among the three algorithms of LSTM, RNN, and BP, LSTM had the lowest MAE, MAPE, and RMSE, and its prediction performance was optimal.