## 5 Experimental Results
### Results and Discussion
#### 5.3.2 Evaluation of Global-Based Predictor Using CAT Approach  
In this experiment, we developed a global predictor trained on consumption readings from all consumers, employing various CAT thresholds ranging from 10% to 30%. This predictor was then compared to a baseline model constructed using the actual non-CAT consumption readings from all consumers. Figures 8-10 illustrate the comparison among different models, including LSTM, transformers, and a combined LSTM-transformer structure.  
The results reveal that the predictor trained on actual readings (non-CAT) achieves the lowest _MAAPE_, indicating superior prediction performance. The introduction of a 10% CAT threshold results in a slight increase in _MAAPE_, signifying a minor decline in prediction accuracy. As the CAT threshold increases, the _MAAPE_ also rises, with the 30% CAT approach yielding the highest error. This trend is attributed to the increased difficulty of prediction operations and the introduction of more errors, as higher CAT thresholds tend to produce readings with more fixed values, making patterns less discernible compared to lower CAT thresholds. Conversely, lower CAT thresholds are associated with reduced errors since they offer more identifiable patterns, thereby enhancing the accuracy of the global predictor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Savings**} & **Approach** & \multicolumn{2}{c}{**CAT**} \\ \cline{2-4}  & **10\%** & **20\%** & **30\%** \\ \hline \(\eta\) & 20.9 & 44.9 & 64.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of savings under CAT approach.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{3}{c}{**Hyperparameters**} \\ \cline{2-4}  & **Architecture Layer** & **\# Units** & **Activation Function** \\ \hline \hline \multirow{6}{*}{Transformer} & Input & 48 & Linear \\ \cline{2-4}  & Multi-Head Attention & 8 & Sigmoid \\ \cline{2-4}  & Normalization Layer & - & - \\ \cline{2-4}  & Multi-Head Attention & 8 & Sigmoid \\ \cline{2-4}  & Normalization Layer & - & - \\ \cline{2-4}  & Output & 1 & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 3: The configuration settings of the transformer-based predictor.  
Without CAT, the _MAAPE_ values are 3.65, 5.19, and 2.22 for the LSTM, transformer, and combined LSTM and transformer models, respectively. As the threshold increases from 10% to 30%, the _MAAPE_ ranges from 3.93 to 4.71 for the LSTM, 5.44 to 8.2 for the transformer, and 3.17 to 3.61 for the combined LSTM and transformer models. LSTM models perform well due to their ability to handle temporal dependencies in time-series data through their memory cells, resulting in low prediction errors. Transformers, while effective in capturing global dependencies through self-attention, struggle with local temporal dynamics. Combining LSTM and transformers leverages LSTM's temporal modeling strengths and transformers' ability to capture global relationships, resulting in improved overall performance and reduced prediction errors.  
Figure 8: MAAPE vs. used transmission approach for LSTM of global-based predictor.  
Figure 9: MAAPE vs. used transmission approach for transformer of global-based predictor.