# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 4 Experimental Design
### Results and Analysis  
To verify the performance of the proposed model, the experiment was carried out on three wind farms. To validate the efficiency of the proposed model, we compared the DWT_AE_BiLSTM model with three models: autoencoder and bidirectional long short-term memory without DWT (AE_BiLSTM), long short-term memory (LSTM) and traditional back propagation (BP). Table 1 presents the forecast performance of each model in Wind Farm #1. The DWT_AE_BiLSTM model demonstrates the highest prediction accuracy (PA) across all the months, with an increase of 6.45% compared with the BP algorithm in February and reaching a maximum of 90.69% in all months by October. DWT_AE_BiLSTM model is 6.45%, 3.59%, 1.7% and 3.7% higher than the BP algorithm in January, April, July and October, respectively, with an average increase of 3.86%, while MAE was decreased by 4.41 MW, 3.79 MW, 4.88 MW and 6.62 MW, respectively. The BP model forecast exhibits lower accuracy in the time periods 2352-2535 and 1259-1342, while it shows significantly greater accuracy in the time periods 961-1002 and 1387-1482, as depicted in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & **Algorithms** & **PA (\%)** & **MAE (MW)** & **MAPE (\%)** \\ \hline \multirow{4}{*}{1} & DWT\_AE\_BiLSTM & 84.69 & 12.03 & 10.94 \\  & AE\_BiLSTM & 83.40 & 13.10 & 11.91 \\  & LSTM & 82.08 & 14.25 & 12.95 \\  & BP & 78.24 & 17.48 & 15.89 \\ \hline \multirow{4}{*}{4} & DWT\_AE\_BiLSTM & 82.36 & 13.58 & 12.35 \\  & AE\_BiLSTM & 81.33 & 15.73 & 14.30 \\  & LSTM & 79.04 & 16.33 & 14.85 \\  & BP & 78.77 & 16.93 & 15.39 \\ \hline \multirow{4}{*}{7} & DWT\_AE\_BiLSTM & 83.63 & 11.65 & 12.41 \\  & AE\_BiLSTM & 82.68 & 12.39 & 11.26 \\  & LSTM & 82.10 & 12.57 & 11.43 \\  & BP & 81.93 & 12.60 & 11.45 \\ \hline \multirow{4}{*}{10} & DWT\_AE\_BiLSTM & 90.69 & 6.34 & 5.76 \\  & AE\_BiLSTM & 89.76 & 7.14 & 6.49 \\ \cline{1-1}  & LSTM & 88.30 & 9.05 & 8.23 \\ \cline{1-1}  & BP & 86.99 & 10.02 & 9.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance evaluation of each prediction algorithm in Wind Farm #1. Figure 4: Comparison among different wind power short-term prediction models of Wind Farm #1 in April 2019. Table 2 illustrates the comparison of the prediction performance of each model in Wind Farm #2. The DWT_AE_BiLSTM model shows the highest PA in January (84.75%) and the lowest in April (82.65%). In April, the BP and LSTM forecast accuracies are similar, while the PA of BiLSTM is 1.26% higher than LSTM, and DWT_AE_BiLSTM is 0.94% higher than AE_BiLSTM, as indicated in Figure 5. In all months, DWT_AE_BiLSTM model is 5.3%, 2.3%, 1.16% and 4.1% higher than the BP algorithm in January, April, July and October, respectively, and 3.22% higher on average in 4 months. Figure 6 shows that in the April forecast for Wind Farm #3, the proposed model DWT_AE_BiLSTM is 2.58%, 2.67%, 3.55% and 4.93% higher than the BP algorithm in January, April, July and October, respectively, and 3.42% higher on average in 4 months. The BP prediction model is significantly higher than the real result in the 471-511, 559-563 and 1538-1662 time periods, the PA of BiLSTM is 0.49% higher than that of LSTM, which also proves that the prediction accuracy can be improved by using an autoencoder feature extraction. After using DWT to denoise, the prediction accuracy was further improved. In addition, Tables 1-3 and Figures 4-6 depict the performance of each model on all evaluation metrics in detail. Among all the models, the DWT_AE_BiLSTM model had the best prediction accuracy in terms of PA, MAE and MAPE. The BP model had the lowest prediction accuracy of all performance indexes. Obviously, through the comparative