## 4 Wind Power Forecast
### Experiment Implementation
For ETT data, the prediction lengths of ETTh1 and ETTh2 were 24, 48, 96, 288, and 720, respectively, and the corresponding prediction time lengths were 24 h, 48 h, 96 h, 288 h, and 720 h. The predicted lengths of ETTm1 were 24, 48, 96, 288, and 672, corresponding to the predicted times of 6 h, 12 h, 24 h, 72 h, and 168 h, respectively. Univariate prediction takes the value of oil temperature, and multivariate prediction takes the values from all variables. (1) Evaluation index  
The Mean Absolute Error (MAE) and Weighted Mean Absolute Percentage Error (WMAPE) were used as evaluation criteria. Because some variables in the task may have had negative values (the data sets in the format as Tables 1 and 2), the optimized WMAPE was calculated as follows,  
\[\text{WMAPE}=\frac{\sum_{i=0}^{\tau}\left|\left.\vec{x}_{i}-\vec{x}_{i}\right| \right.}{\sum_{i=0}^{\tau}\left|\vec{x}_{i}\right|} \tag{3}\]  
where \(\tau\) is the total number of data points and \(\left.\vec{x}_{i}\right.\) is the mean of the data in the sample. The relative improvement of performance (RIP) with MAE and absolute improvement of performance (AIP) with WMAPE were used for comparison. They were calculated as follows:  
\[\text{RIP}=\frac{\text{MAE}_{\text{O}}-\text{MAE}}{\text{MAE}_{\text{O}}}, \tag{4}\]  
\[\text{AIP}=\text{WMAPE}_{\text{o}}-\text{WMAPE}, \tag{5}\]  
where \(\text{MAE}=\frac{1}{\tau}\sum\limits_{i=0}^{\tau}\left|\left.\vec{x}_{i}- \vec{x}_{i}\right|\right.\), \(\text{MAE}_{\text{o}}\) and \(\text{WMAPE}_{\text{o}}\) are obtained using other competitive methods for prediction, and MAE and WMAPE are obtained using the newly proposed method based on the SFINet model. (2) Data processing  
In order to evaluate the performance of our proposed algorithm in wind turbine power prediction, we conducted experiments based on the proposed WP data sets and compared the prediction results with those given by the SCINet models. At the same time, in order to further verify the general applicability of the algorithm, we also verified the performance of the algorithm using the ETT data sets and compared it with other methods, including SCINet. Our task does not specify the look-back windows corresponding to a certain prediction sequence horizon. In the wind power forecasting task, the original wind power data have singular values. In order to eliminate the singular values shown in Table 1, we chose to skip singular values when constructing the training, validation, and testing for different tasks. If there was a singular value in a pair, this piece of data was discarded. Therefore, in this way, for different tasks, the number of samples for training, validation, and testing were be produced, as shown in Table 4. Table 4 also shows the input data time length for a certain prediction length; for example, if the prediction length is for 6 h, the input data covers a time length of 128 h.  
The singular value issue did not appear in the ETT data set, and these data could be used normally. In the ETT task, the settings are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Task**} & \multicolumn{5}{c}{**Wpm1**} & \multicolumn{5}{c}{**Wpm2**} \\ \hline Length & Horizon & 6 & 12 & 24 & 48 & 6 & 12 & 24 & 48 \\  & look-back & 128 & 256 & 512 & 512 & 128 & 256 & 512 & 512 \\ \hline \multirow{3}{*}{Number of samples} & Training & 24,150 & 22,810 & 20,248 & 20,032 & 24,400 & 23,418 & 21,388 & 21,220 \\  & Validation & 10,862 & 10,722 & 10,442 & 10,394 & 10,862 & 10,722 & 10,442 & 10,394 \\  & Test & 15,033 & 14,625 & 13,809 & 13,713 & 15,043 & 14,635 & 13,819 & 13,723 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Wind power forecasting task setting and number of samples. All data were normalized. In terms of the loss function and optimizer, we followed the same settings for the SCINet model given in [23]. 1. Hardware platform linebreak Training GPU: Single Nvidia 3080 Ti 16 GB. CPU Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20 GHz. Memory: 256 G.