# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 5 Conclusions  
This paper proposes an innovative CNN-LSTM model with a multimodal attention mechanism to address the nonlinearity and randomness in short-term power grid load forecasting. Power grid load data exhibit complex spatial and temporal dependencies, requiring robust models with strong expressive power. The proposed model integrates CNN, LSTM, and multiple attention mechanisms to explore load data from different dimensions. The CNN module extracts spatial features through convolution operations, capturing load patterns, while the LSTM layer captures long-term dependencies and short-term fluctuations, handling the time-varying nature of load data. A multi-head self-attention mechanism is introduced to strengthen global feature modeling by computing attention heads in parallel, overcoming the limitations of a single attention mechanism. Additionally, global attention focuses on key time-step features, improving temporal representation, while channel attention optimizes feature selection by suppressing irrelevant information. The experimental results show that the model significantly outperforms traditional and deep learning methods in terms of prediction accuracy, stability, and generalization. On the three benchmark datasets, compared to the best baseline models, our proposed model \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Heads** & **MAE** & **MSE** & **RMSE** & **Max Error** & **Cost Time (s)** \\ \hline \multirow{8}{*}{Panama Case Study Dataset} & 1 & 85.25 & 14,268.92 & 119.45 & 635.26 & 635.2 \\  & 2 & 83.38 & 12,896.36 & 113.56 & 584.52 & 1026.5 \\  & 4 & 81.52 & 12,236.59 & 110.62 & 558.65 & 1335.6 \\  & 8 & 81.36 & 12,185.45 & 110.38 & 552.12 & 1523.2 \\  & 16 & 81.02 & 12,093.28 & 109.96 & 542.68 & 2120.5 \\ \hline \multirow{8}{*}{Victoria State Dataset} & 1 & 0.153 & 0.042 & 0.205 & 0.78 & 539.6 \\  & 2 & 0.131 & 0.035 & 0.187 & 0.62 & 956.5 \\ \cline{1-1}  & 4 & 0.115 & 0.027 & 0.164 & 0.50 & 1126.3 \\ \cline{1-1}  & 8 & 0.112 & 0.024 & 0.155 & 0.48 & 1386.6 \\ \cline{1-1}  & 16 & 0.106 & 0.018 & 0.134 & 0.46 & 1835.4 \\ \hline \multirow{8}{*}{Household Dataset} & 1 & 0.96 & 1.21 & 1.10 & 4.96 & 658.6 \\ \cline{1-1}  & 2 & 0.85 & 1.06 & 1.03 & 4.62 & 1005.2 \\ \cline{1-1}  & 4 & 0.76 & 0.96 & 0.98 & 4.12 & 1263.5 \\ \cline{1-1}  & 8 & 0.72 & 0.93 & 0.96 & 4.08 & 1638.8 \\ \cline{1-1}  & 16 & 0.71 & 0.91 & 0.95 & 4.05 & 2135.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison across different heads for various datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Method** & **MHSA** & **GAM** & **CAM** & **MAE** & **MSE** & **RMSE** & **Max Error** \\ \hline \multirow{4}{*}{Panama Case Study Dataset} & CNN-LSTM+ & & & & 89.62 & 15, 285.86 & 123.64 & 780.36 \\ \cline{2-8}  & CNN-LSTM+ & ✓ & & & 86.36 & 14,626.77 & 120.94 & 652.55 \\ \cline{2-8}  & CNN-LSTM+ & ✓ & ✓ & & 83.95 & 13,168.68 & 114.75 & 583.26 \\ \cline{2-8}  & CNN-LSTM+ & ✓ & ✓ & ✓ & 81.52 & 12,236.59 & 110.62 & 558.65 \\ \hline \multirow{4}{*}{Victoria State Dataset} & CNN-LSTM+ & & & & 0.141 & 0.044 & 0.210 & 0.78 \\ \cline{2-8}  & CNN-LSTM+ & ✓ & & & 0.135 & 0.038 & 0.195 & 0.69 \\ \cline{1-1} \cline{2-8}  & CNN-LSTM+ & ✓ & ✓ & & 0.128 & 0.033 & 0.182 & 0.61 \\ \cline{1-1} \cline{2-8}  & CNN-LSTM+ & ✓ & ✓ & ✓ & 0.115 & 0.027 & 0.164 & 0.50 \\ \hline \multirow{4}{*}{Household Dataset} & CNN-LSTM+ & & & & 0.93 & 1.27 & 1.13 & 5.25 \\ \cline{1-1} \cline{2-8}  & CNN-LSTM+ & ✓ & & & 0.88 & 1.19 & 1.09 & 4.88 \\ \cline{1-1} \cline{2-8}  & CNN-LSTM+ & ✓ & ✓ & & 0.82 & 1.08 & 1.03 & 4.53 \\ \cline{1-1} \cline{2-8}  & CNN-LSTM+ & ✓ & ✓ & & 0.76 & 0.96 & 0.98 & 4.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results for attention mechanisms on different datasets. (✓ indicates that the corresponding attention mechanism was applied in the experiment.