## 4 Wind Power Forecast
### Prediction Experiment and Result Analysis  
1. Wind power forecasting  
Applying SFINet to the wind power data sets, the forecasting performance obtained is shown in Table 6. It can be seen from Table 6 that the prediction results of the SFINet model proposed in this paper were generally better than those of the SCINet model. The evaluation criteria of MAE using the multivariate and univariate prediction results with the Wpm1 data set were reduced by up to 10.07% and 6.90%, respectively; and up to 9.20 and 6.87%, respectively, for the Wpm2 data set. The forecast results evaluated using WMAPE are shown in Table 7. From the results given in Table 7, the superiority of the SFINet model algorithm was verified in univariate and multivariate wind turbine power prediction. The evaluation indices of WMAPE with the Wpm1 data set were reduced by up to 5.79% using multivariate evaluation and 4.93% using univariate evaluation, and the WMAPE with the Wpm2 data set were reduced by up to 4.86 and 4.35%, respectively. The performance improvement trend of the SFINet model under different horizon tasks was similar to the results using MAE metric. We then performed qualitative analysis of the prediction results using the wind power data set by selecting a piece of wind turbine power data for a sampling length of 200 in the WPh1 data set and WPh2 data set, as shown in Figure 5a,b. It can be seen that the forecasted wind power had the characteristics of large variation, violent fluctuations and no obvious laws to follow. The prediction result of the SFINet model could better fit the actual power curve. At the same time, at the peak and valley points of the power change, the prediction result of the SFINet model was generally better than that of the SCINet model (see also Figure 5c) by using the published data set. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**WPm1**} & \multicolumn{4}{c}{**WPm2**} \\  & & \multicolumn{4}{c}{**Horizon**} & \multicolumn{4}{c}{**Horizon**} \\ \cline{3-10}  & & **6** & **12** & **24** & **48** & **6** & **12** & **24** & **48** \\ \hline \multirow{4}{*}{Multivariate} & SCINet & 0.287 & 0.353 & 0.4359 & 0.487 & 0.277 & 0.3252 & 0.4161 & 0.4978 \\  & SFINet & 0.276 & 0.327 & 0.392 & 0.452 & 0.2709 & 0.3191 & 0.384 & 0.452 \\ \cline{1-1}  & RIP & 3.83\% & 7.37\% & 10.07\% & 7.19\% & 2.20\% & 1.88\% & 7.71\% & 9.20\% \\ \cline{1-1}  & SCINet & 0.2351 & 0.3059 & 0.4032 & 0.5071 & 0.2319 & 0.3028 & 0.4059 & 0.4978 \\ \cline{1-1}  & SFINet & 0.2291 & 0.2912 & 0.379 & 0.4721 & 0.2241 & 0.2851 & 0.378 & 0.4733 \\ \cline{1-1}  & RIP & 2.55\% & 4.81\% & 6.00\% & 6.90\% & 3.36\% & 5.85\% & 6.87\% & 4.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Forecasting results evaluated in MAE on wind power data sets. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Task**} & \multicolumn{4}{c}{**ETTh1**} & \multicolumn{4}{c}{**ETTh2**} & \multicolumn{4}{c}{**ETTm1**} \\ \hline \multirow{2}{*}{Length} & Horizon & 24 & 48 & 168 & 336 & 720 & 24 & 48 & 168 & 336 & 720 & 24 & 48 & 96 & 288 & 672 \\  & look-back & 48 & 96 & 336 & 672 & 1440 & 48 & 96 & 336 & 672 & 1440 & 96 & 96 & 384 & 672 & 672 \\ \hline \multirow{3}{*}{Number of samples} & Training & 8569 & 8497 & 8137 & 7633 & 7185 & 8569 & 8497 & 8137 & 7633 & 7185 & 34,441 & 34,417 & 34,081 & 33,601 & 33,217 \\  & validation & 2857 & 2833 & 2713 & 2545 & 2161 & 2857 & 2833 & 2713 & 2545 & 2161 & 11,497 & 11,473 & 11,425 & 11,233 & 10,849 \\ \cline{1-1}  & test & 2857 & 2833 & 2713 & 2545 & 2161 & 2857 & 2833 & 2713 & 2545 & 2161 & 11,497 & 11,473 & 11,425 & 11,233 & 10,849 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ETT task setting and number of samples. Generalization study  
The ETT data set given in [55] were used to evaluate the performance of a time series forecasting task e.g., [23; 55].