## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Reference** [33] & \multicolumn{4}{c}{**This Study**} \\ \hline
**Features** & 4 & 10 & 6 \\ \hline
**Sequence window** & 168 [h] & 24 [h] & \multicolumn{4}{c}{} \\ \hline
**Data size** & 1456 & 8723 & & 22,328 & 42,700 & 84,617 \\ \hline  & layers & \multicolumn{2}{c}{RSME} & layers & \multicolumn{2}{c}{RSME} \\ \hline
**LSTM** & 2 & 0.197 & 0.097 & 5 & 0.04939 & 0.056421 & **0.044879** \\ \hline
**ConvLSTM** & 7 & 0.207 & 0.053 & 7 & 0.055561 & 0.057934 & **0.046501** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the present study with similar study based on RSME. Figure 20: Average errors in three datasets. By repeating the experiment, 50% of the dataset always shows the highest errors. This suggests that there is an optimal dataset size for each model regarding their depth (layers), and increasing or decreasing the data beyond that point might lead to overfitting. This finding emphasizes the need for careful data curation and model selection based on the ANN model, depth, dataset size, and features. These findings are aligned with [69], which emphasizes the threshold in which CNN models perform the best regarding training data size. With the lowest dataset, all the univariable models perform better than the multivariable. This implies that when the range of data training is lower than 22,328, the feature inclusion does not improve the performance of ANN models. Moreover, including additional features is beneficial for Conv2D and FCN models when increasing the dataset from 42,700 to 84,617. However, this improvement is not observed when increasing the dataset from 22,000 to 42,700. When increasing the dataset from 22,328 to 42,700 only ConvLSTM and Conv1D performed better when including features, but this advantage diminishes when further increasing the dataset to 84,617. Considering these findings, it can be concluded that there is no consistent pattern or relationship observed among the ANN models when it comes to the inclusion of features. These findings are aligned with the results of reference [33; 49], as they used 32 and 10 features in their study. Apart from the training data size, the number of layers contribute to the performance of ANN models. For example, in reference [33], just one LTSM layer with one dense layer is used, and they get a root mean square error (RMSE) of 0.097, which can be improved significantly by adding more layers. In addition, they achieved an RSME of 0.053 for ConvLSTM, which is more than the result of this study (0.046501). Apart from the data training size, the present study confirms that ConvLSTM performs better without including the features; thus, another reason might be the lower number of features in the present study--six compared to ten features in [33]. Including more features does not improve the performance of all other models, except LSTM, Conv2D, and FCN, which is confirmed by reference [70] where they concluded that adding more features does not always lead to better performance. [PERSON] et al. [71] in a paper called \"Are Transformers Effective for Time Series Forecasting?\" have discussed the inefficiency of Transformers in time series forecasting. This inefficiency is due to temporal information loss, which is the result of the permutation invariant self-attention mechanism. In other words, in time series forecasting, the sequence order often plays an important role in which attention mechanism of Transformers is not considered. The results of this study confirm this argument, as the Transformer has the worst performance in all datasets. The present study takes the training time into account as an important factor in selecting an ANN model in time series prediction tasks such as power load forecasting. The CNN models are among the fastest models, where Conv2D multivariable is the fastest model, followed by Conv2D univariable, Conv1D univariable, and Conv1D multivariable.