## 5 Experiments
### Comparative Experiments on Different Time Scales  
In order to further verify the predictive performance of the proposed model at different time scales, further experiments were conducted in this paper. The input sequence length and prediction sequence length were set to \(\{4,12,24,48,96,192,384\}\). The prediction accuracy results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{4}{c}{**With NWP**} & \multicolumn{4}{c}{**Without NWP**} \\ \hline
**Length** & \multicolumn{2}{c}{**Short Term**} & \multicolumn{2}{c}{**Medium-Long-Term**} & \multicolumn{2}{c}{**Short Term**} & \multicolumn{2}{c}{**Medium-Long-Term**} \\ \hline
**Metric** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline DNN & \(0.0721^{+0.0008}_{-0.0012}\) & \(0.1641^{+0.0061}_{-0.0025}\) & \(0.6491^{+0.0116}_{-0.0050}\) & \(0.5498^{+0.0063}_{-0.0165}\) & \(0.0747^{+0.0019}_{-0.0020}\) & \(0.1701^{+0.0080}_{-0.0054}\) & \(1.1728^{+0.0037}_{-0.0066}\) & \(0.9143^{+0.0032}_{-0.0032}\) \\ GRU & \(0.0599^{+0.0015}_{-0.0023}\) & \(0.1436^{+0.0025}_{-0.0028}\) & \(0.5626^{+0.0012}_{-0.0020}\) & \(0.5083^{+0.0248}_{-0.0214}\) & \(0.0641^{+0.0010}_{-0.0000}\) & \(0.1482^{+0.0049}_{-0.0008}\) & \(1.2014^{+0.0112}_{-0.0089}\) & \(0.9182^{+0.0052}_{-0.0005}\) \\ TCN & \(0.0636^{+0.0018}_{-0.0018}\) & \(0.1432^{+0.0027}_{-0.0027}\) & \(0.5308^{+0.0012}_{-0.0012}\) & \(0.5661^{+0.0030}_{-0.0030}\) & \(0.0645^{+0.0010}_{-0.0010}\) & \(0.1495^{+0.0072}_{-0.0072}\) & \(1.1538^{+0.0017}_{-0.0032}\) & \(0.9031^{+0.0029}_{-0.0025}\) \\ V-Former & \(0.0675^{+0.0054}_{-0.0054}\) & \(0.1651^{+0.0116}_{-0.0048}\) & \(0.5756^{+0.0131}_{-0.0029}\) & \(0.5316^{+0.0057}_{-0.0057}\) & \(0.0761^{+0.0048}_{-0.0050}\) & \(0.1672^{+0.0048}_{-0.0048}\) & \(1.2603^{+0.0175}_{-0.0127}\) & \(0.9248^{+0.0139}_{-0.0226}\) \\ Encoder-only & \(0.0615^{+0.0022}_{-0.0024}\) & \(0.1471^{+0.0007}_{-0.0027}\) & \(0.5315^{+0.0048}_{-0.0048}\) & \(0.5419^{+0.0127}_{-0.0027}\) & \(0.0650^{+0.0100}_{-0.0010}\) & \(0.1458^{+0.0018}_{-0.0048}\) & \(1.1 It can be seen from the table that the length of the input and prediction sequences directly affects the accuracy of the prediction model, both when the input sequence is too short and the prediction sequence is too long. The model is unable to extract sufficient feature information from short sequences and is, thus, unable to achieve long-distance power prediction. When the input sequence is too long and the prediction sequence is relatively short, the data features of the input model are not concise enough, with a large amount of useless and redundant information, which can interfere with the model's judgment. The model's prediction performance is best when the lengths of the input and output sequences are within a reasonable range. Among them, for short-term prediction, a 24-step input sequence is sufficient to complete the model prediction. For medium- to long-term prediction models, if the length of the input sequence is generally consistent with the length of the predicted length sequence, the effect is best. In order to further observe the predictive effect of the proposed model on different input and output sequence lengths, and achieve visualization of the output, this paper conducted experiments on typical prediction tasks \(\{\text{L}_{seq}\!=\!24,\text{L}_{pred}\!=\!4\}\), \(\{\text{L}_{seq}\!=\!24,\text{L}_{pred}\!=\!24\}\,\{\text{L}_{seq}\!=\!96, \text{L}_{pred}\!=\!24\}\), \(\{\text{L}_{seq}\!=\!96,\text{L}_{pred}\!=\!96\}\). Furthermore, it randomly selected four sets of experimental data \(\{\text{A},\text{B},\text{C},\text{D}\}\) to output prediction waveforms and Encoder attention maps. Each group of experimental results consists of an attention map of the input sequence and a comparison map of actual power and predicted power. The results are shown in Figure 9. It can be observed that the proposed scheme has good prediction performance at different time scales.