## 5 Experiments
### Experimental Results
#### 5.2.3 Ablation Study  
To evaluate the effectiveness of each component in STGPT2 UGAN, we design the following variants:  
* _Basic:_ The variant forecasts the future wind speed sequences using a two-layer FC without the activation function. The learning rate \(L_{r}\) is \(1\times 10^{-3}\). * +_TA:_ It is equipped with temporal attention in Basic. The number of temporal attention layers \(L_{TA}\) is one. The number of attention heads \(N_{z}\) is four. The dimensions of each head \(d\) is eight. * +_SA:_ The variant adds spatial attention in the +TA. The number of spatial attention layers \(L_{SA}\) is one. * +_TGPT-2:_ the variant introduces TGPT-2 into the +SA. * _STGPT2 UGAN:_ in contrast to +TGPT-2, STLLMUGAN integrates GAN into +TGPT-2. To ensure fairness, STGPT2 UGAN and other variants have the same parameter settings, except for the aforementioned differences. As shown in Table 3, the wind speed prediction results over the next 1-4 h, 5-8 h, and 9-12 h on the DSWE 1 and NREL datasets are presented. Basic can only capture linear correlations, while nonlinear temporal dependencies fail to be extracted. Hence, the results in each prediction timestep have the same error. In contrast to Basic, +TA mines temporal dependencies. Accordingly, its prediction error is no longer constant but increases as the prediction timesteps lengthen. +SA is able to simultaneously model spatial dependencies and temporal dependencies, thus further reducing prediction error. +TGPT-2 predicts future wind speed sequences through abundant intrinsic knowledge in pre-trained GPT-2. This enhances prediction precision. By comparing +TGPT-2 and STGPT2 UGAN, we find that STGPT2 UGAN brings about an adversarial training scheme by introducing a GAN. In the adversarial training scheme, the supervised training enables STGPT2 UGAN to align true values and predicted values through minimizing the MSE loss. The unsupervised training enables STGPT2 UGAN to learn the distribution of true data by minimizing the adversarial loss. Therefore, STGPT2 UGAN acquires more comprehensive training, enhancing its prediction accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**1–4 h**} & \multicolumn{2}{c}{**5–8 h**} & \multicolumn{2}{c}{**9–12 h**} & \multicolumn{2}{c}{**Average**} \\  & & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline \multirow{8}{*}{DSWE 1} & ARIMA & 2.21 & 1.86 & 2.75 & 2.26 & 3.24 & 2.61 & 2.73 & 2.24 \\  & SVR & 2.16 & 1.71 & 2.66 & 2.15 & 3.13 & 2.51 & 2.65 & 2.12 \\  & LSTM & 2.06 & 1.59 & 2.53 & 2.01 & 3.05 & 2.44 & 2.55 & 2.01 \\  & CNN-LSTM & 1.98 & 1.52 & 2.43 & 1.95 & 2.97 & 2.38 & 2.46 & 1.95 \\  & GCN-LSTM & 1.86 & 1.47 & 2.48 & 1.90 & 2.88 & 2.32 & 2.41 & 1.90 \\  & GAT-Informer & 1.78 & 1.39 & 2.36 & 1.88 & 2.79 & 2.25 & 2.31 & 1.84 \\  & FPT & 1.65 & 1.24 & 2.28 & 1.73 & 2.70 & 2.16 & 2.21 & 1.71 \\  & LLM4 TS & 1.69 & 1.25 & 2.26 & 1.77 & 2.68 & 2.13 & 2.21 & 1.72 \\  & STGPT2 UGAN & **1.59** & **1.20** & **2.17** & **1.66** & **2.62** & **2.07** & **2.13** & **1.64** \\ \hline \multirow{8}{*}{NREL} & ARIMA & 3.88 & 2.95 & 5.01 & 3.97 & 6.03 & 4.92 & 4.97 & 3.95 \\  & SVR & 3.75 & 2.86 & 4.92 & 3.83 & 5.94 & 4.81 & 4.87 & 3.83 \\  & LSTM & 3.42 & 2.57 & 4.61 & 3.56 & 5.59 & 4.48 & 4.54 & 3.54 \\  & CNN-LSTM & 3.37 & 2.66 & 4.55 & 3.54 & 5.53 & 4.42 & 4.48 & 3.54 \\  & GCN-LSTM & 3.35 & 2.54 & 4.48 & 3.55 & 5.46 & 4.39 & 4.43 & 3.49 \\  & GAT-Informer & 3.30 & 2.53 & 4.32 & 3.43 & 5.37 & 4.31 & 4.33 & 3.42 \\  & FPT & 3.26 & 2.48 & 4.24 & 3.36 & 5.25 & 4.19 & 4.25 & 3.34 \\  & LLM4 TS & 3.24 & 2.46 & 4.17 & 3.28 & 5.16 & 4.07 & 4.19 & 3.27 \\  & STGPT2 UGAN & **3.21** & **2.45** & **4.03** & **3.19** & **5.08** & **4.02** & **4.11** & **3.22** \\ \hline \end{tabular}
\end{table} 
Table 2: The comparison of prediction accuracy for STGPT2 UGAN and advanced benchmarks on DSWE 1 and NREL datasets.