## 4 Discussion  
In this study, eight of the state-of-the-art ANN models for power load prediction are evaluated based on available dataset size (entire dataset, 50% and 25% of dataset) and the inclusion of features (univariable vs. multivariable). Due to the scarcity of comparable studies, the result of the present study is compared with aggregated comparable studies. The results of some of these studies where scales of data are the same were normalized for load prediction with relevant sequence time windows [48; 49]. In addition, the number of hidden layers and other hyperparameters such as Adam optimiz  
Figure 17: Shapely values of features in LSTM multivariable model. the results can be somehow compared, as shown in Tables 6 and 7. As can be seen from the result of reference [51] where the models are trained on 367,920 datapoints (biggest data size), GRU performs worse, which explains the increase in their error in the current study, with an MAE of 0.008551 for univariable and 0.033668 for multivariable in the entire dataset compared to 0.00471 and 0.00685 in 25% of the dataset. Thus, it can be concluded that for the time series, the training data are approximately in the range of 2000 and 22,000, The GRU is the best choice in shallow networks  
Including more training data the performance of LSTM, Conv1D, and Conv2D improved significantly as they have a lower error with training on 84,617 data than the same models in reference [48], which are trained on 59,829 data. Therefore, it can be concluded that when the training data are between 840,00 and 367,920, LSTM, ConvLSTM, Conv1D, and Conv2D are good choices in terms of accuracy, as confirmed by the results of reference [51]. Surprisingly, when increasing the data from 25% to 50% of the data (42,700), the ANN models perform worse, contrary to the assumption that more training data leads to better performance (see Figure 20). The GRU is excluded from Figure 20 due to its high error in the entire dataset, moving the average error with more than 25% of the dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  &
\begin{tabular}{c} **Reference** \\ [31] \\ \end{tabular} & **Reference** [48] & **Reference** [49] & **Reference** [51] & \multicolumn{4}{c}{**This Study**} \\ \hline
**Features** & 4 & 1 & 32 & 6 & 1 & 6 & 1 & 6 & 1 & 6 \\ \hline
**Sequence** & 120 [h] & 20 [h] & 1 [h] & 15 [min] & \multicolumn{4}{c}{24 [h]} \\ \hline
**Training** & 2208 & 59,829 & 77,831 & **367,920** & 22,328 & 22,328 & 42,700 & 42,700 & 84,617 & 84,617 \\ \hline  & Layers MAE & Layers MAE & Layers MAE & Layers MAE & Layers MAE & Layers MAE & \multicolumn{4}{c}{MAE} \\ \hline
**Conv1D** & & 5 & 0.008269 & & & 6 & 0.00514 & 0.00614 & 0.01142 & 0.00916 & **0.004626** & 0.005758 \\ \hline
**Conv2D** & & 4 & 0.0088 & & & 6 & 0.00528 & 0.00639 & 0.00861 & 0.00876 & 0.005212 & **0.005036** \\ \hline
**ConvLSTM** & * & 5.061 & 6 & 0.006624 & 11 & 0.0057 & & 6 & 0.00541 & 0.00617 & 0.01252 & 0.00847 & **0.00455** & 0.004764 \\ \hline
**LSTM** & * & 6.009 & 4 & 0.007345 & & 4 & 0.55 & 4 & 0.00482 & 0.00515 & 0.0075 & 0.0112 & 0.004886 & **0.004521** \\ \hline
**BiLSTM** & & & & 5 & 0.0066 & 4 & 0.8296 & 4 & 0.00549 & 0.00738 & 0.00788 & 0.01032 & **0.005101** & 0.005625 \\ \hline
**GRU** & & & & & 4 & 1.3269 & 5 & **0.00471** & 0.00685 & 0.00912 & 0.01032 & 0.008551 & 0.033668 \\ \hline \multicolumn{10}{l}{* **Is not given in the reference**} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the present study with some similar studies based on MAE.