# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 4 Results and Discussion
Color coding uses a color palette where the greenest coloris for the best result and the reddest is for the worst. Color coding was applied column-wise to compare results for the same metric. Figure 6 presents all the results for the Seq2 seq and Seq2 seq + attention models. Figures 7 and 8 present the results for the rest of the models, Figure 7 for a \(f\) value equal to 45, and Figure 8 for an \(f\) value equal to 1. Metric values are provided for the forecasts of the first (T-0) and the last (T-23) time-slots, in addition to the average of forecasts for all (24) predicted time-slots. The last two columns provide the training and test times for all models. Figure 7: (**Upper section**): forecast metrics for the average, first (T-0), and last (T-23) predicted time-slots using DL, ML, and ensemble models for a fixed number of values per predictor (\(f=45\)). The table is color-coded along columns with colors between red and green corresponding to the worst and best results, respectively. (**Lower section**): bar chart for sMAPE metrics for some of the best performing models from the upper table. The best results for the Seq2 seq models were achieved when using only recurring networks (one or two LSTM layers) while the inclusion of convolutional layers deteriorates the forecast. Seq2 seq models (Figures 6 and A1 (Appendix A) present some of the best results for very short-term forecasts and worst for average and long-term forecasts. These models have difficulty converging with \(f\), equal to one with their best results with \(f\) equal to 45. The results for DL, ML, and ensemble models, with an \(f\) value equal to 45, are shown in Figure 7 and the Appendix A. For this value of parameter \(f\), the best performing models  
Figure 8: (**Upper section**): forecast metrics for the average, first (T-0), and last (T-23) predicted time-slots using DL, ML, and ensemble models for a fixed number of values per predictor (\(f=1\)). The table is color-coded along columns with colors between red and green corresponding to the worst and best results, respectively. (**Lower section**): bar chart for sMAPE metrics for some of the best performing models from the upper table. were classic ML models. A combination of two CNN layers and two LSTM appeared to provide a performance similar to random forest and linear regression. The ensemble models did not give a good performance either. The results for DL, ML, and ensemble models, with an \(f\) value equal to 1, are shown in Figure 8 and the Appendix A. The results in this figure are very different from the results in Figure 7, despite being the same models with a different value for parameter \(f\). In Figure 8, we obtain most of the best results obtained in this work. The best performing model types were DL and ensemble models for long-term forecasting and average results, while for short-term results, the classic ML and Seq2 seq models were best. It is interesting how the combination of CNN and LSTM layers produced some of the best performance models, since this result is very different from what happens in Seq2 seq architectures, for which these layer combinations provide very poor results. A conclusion drawn from the results in Figures 6-8 is the importance of the impact of the rolling window length and the number of features associated with the time-slots. Another is that the best specific sequence of layers for a deep learning architecture depends on the type of model, making it impossible to establish a priori a better architecture for all types of models. In all the tables in Figures 6-8, the description of the models includes the number and type of layers used: CNN, LSTM, and fully connected layers (FC), forming a sequence separated by the + sign. The ensemble models follow the gaNet architecture [11] which is formed by repeating blocks where the configuration of each repeating block is included in parentheses with an asterisk and a number to the right of the parentheses that indicates the number of repetitions of the block.