# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 4 Case Study
### Comparative Experiments
The results indicate that the proposed model outperforms other comparison models for all metrics across the three datasets. Specifically, in the Panama Case Study Dataset, the MAE is 81.52, MSE is 12,236.59, RMSE is 110.62, and the Max Error is 558.65; in the Victoria State Dataset, the MAE is 0.115, MSE is 0.027, RMSE is 0.164, and Max Error is 0.50; and in the Household Dataset, the MAE is 0.76, MSE is 0.96, RMSE is 0.98, and Max Error is 4.12. The performance of all three datasets is significantly better than that of traditional models (such as ANN, RNN, LSTM, and GRU) as well as hybrid models (such as CNN-RNN, CNN-LSTM). This advantage is attributed to the introduction of the multimodal attention mechanism, which combines multi-head self-attention, global attention, and channel attention, significantly improving the model's ability to capture spatiotemporal features and global dependencies, while optimizing the structural design to balance forecasting accuracy and model complexity. Figure 5 visually demonstrates the forecasting performance of our model, showing that our model has the lowest MAE, RMSE, and Max Error metrics. In order to improve visualization, the real load values and the predictions of different models on part of the test sets from three datasets were plotted, as shown in Figures 6-8. Among them, (a) shows a comparison of weekly load prediction results, which indicates that our model has a better overall prediction performance, with its predicted values being closer to the real values. Additionally, (b) presents the load prediction results for one specific day. It can be concluded that the proposed model accurately captures the abrupt changes in the load sequence and achieves acceptable prediction results, which are in good agreement with the real values overall. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **MAE** & **MSE** & **RMSE** & **Max Error** \\ \hline \multirow{8}{*}{Panama Case Study Dataset} & ANN & 95.25 & 17, 968.25 & 134.05 & 689.25 \\  & RNN & 93.68 & 17,263.65 & 131.39 & 687.95 \\  & LSTM & 91.16 & 16,856.54 & 129.83 & 706.46 \\  & BiLSTM & 97.55 & 17,315.95 & 131.59 & 718.15 \\  & GRU & 91.89 & 16,265.76 & 127.54 & 856.78 \\  & CNN-RNN & 90.18 & 15,625.36 & 125.00 & 810.26 \\  & CNN-LSTM & 89.62 & 15,285.86 & 123.64 & 780.36 \\  & SBACT-Seq2 Seq & 88.48 & 147,758.58 & 121.48 & 654.59 \\  & EWT-CNN-S-RNN + LSTM & 87.95 & 13,863.84 & 117.74 & 664.28 \\  & Ours & 81.52 & 12,236.59 & 110.62 & 558.65 \\ \hline \multirow{8}{*}{Victoria State Dataset} & ANN & 0.168 & 0.048 & 0.219 & 0.71 \\  & RNN & 0.156 & 0.046 & 0.214 & 0.75 \\  & LSTM & 0.145 & 0.052 & 0.228 & 0.82 \\  & BiLSTM & 0.173 & 0.048 & 0.219 & 3.21 \\  & GRU & 0.142 & 0.045 & 0.212 & 0.79 \\  & CNN-RNN & 0.143 & 0.047 & 0.217 & 0.80 \\  & CNN-LSTM & 0.141 & 0.044 & 0.210 & 0.78 \\  & SBACT-Seq2 Seq & 0.138 & 0.041 & 0.202 & 0.76 \\  & EWT-CNN-S-RNN + LSTM & 0.135 & 0.038 & 0.195 & 0.70 \\  & Ours & 0.115 & 0.027 & 0.164 & 0.50 \\ \hline \multirow{8}{*}{Household Dataset} & ANN & 1.03 & 1.28 & 1.13 & 5.21 \\  & RNN & 0.98 & 1.17 & 1.08 & 5.36 \\  & LSTM & 0.97 & 1.06 & 1.03 & 5.01 \\  & BiLSTM & 1.12 & 1.88 & 1.37 & 5.56 \\  & GRU & 0.88 & 1.28 & 1.13 & 5.05 \\  & CNN-RNN & 0.91 & 1.25 & 1.12 & 5.16 \\  & CNN-LSTM & 0.93 & 1.27 & 1.13 & 5.25 \\  & SBACT-Seq2 Seq & 0.86 & 1.16 & 1.08 & 4.89 \\  & EWT-CNN-S-RNN + LSTM & 0.85 & 1.13 & 1.06 & 4.76 \\  & Ours & 0.76 & 0.96 & 0.98 & 4.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative performance comparison of different models on three datasets. Figure 5: The performance comparison of different models on three datasets. Figure 6: The load prediction results obtained from experiments involving the Panama Case Study Dataset. (**a**) shows a comparison of weekly load prediction results, and (**b**) presents the load prediction results for one specific day. Figure 7: The load prediction results obtained from experiments involving the Victoria State Dataset.