## 3 Results and Discussion
### Experimental Results  
The time series data input for hourly power consumption is initially decomposed by the RobustSTL method to obtain three components. The decomposition results are shown in Figure 5. The figure consists of four parts, i.e., original dataset of power consumption, trend component, seasonality component, and residual component. The x-axis denotes the period of the data, and the y-axis denotes the power consumption value in GWh (Gigawatt Hour). These trend, seasonality, and residual components become the input data for the TCN. Then, Table 1 and Figure 6 show the comparison results of hourly power consumption prediction between the proposed model and the counterpart models in MAPE, MAE, and RMSE metrics. In the comparison, we present LSTM and GRU (gated recurrent units) as the single model, and STL-GRU and RobustSTL-CNN as the hybrid model. In [20], the authors proposed LSTM as a machine learning approach in time series stock data forecasting. The proposed approach, LSTM, outperforms the traditional algorithms in empirical results. GRU was proven to achieve satisfactory accuracy in primary energy consumption forecasting over other methods [21]. A combination of recurrent neural networks and decomposition methods utilizing GRU and STL in short-term power consumption forecasting was proposed [7]. This model obtains good prediction accuracy capturing both local and global information of the data. A hybrid model of CNN and STL, instead of RobustSTL, introduced in [22] also is adopted here. Based on Table 1 above, the proposed model has the lowest value of MAPE (1.95), while the other models mostly have a value of more than 2.00. The proposed model also has the lowest value of MAE and RMSE (0.58 and 0.85), while the other models gain values more than the proposed model values in these metrics. The lowest values in MAPE, MAE, and RMSE metrics indicate the best results because they have lower errors between the predicted and actual values. We can see in Table 1 that models with the hybrid approach outperform the single models in MAPE, MAE, and RMSE metrics. In the single models, GRU is better than LSTM in MAPE and RMSE metrics. LSTM only outperforms GRU in the MAE metric, by a small margin of 0.1. The best model after the proposed model is RobustSTL-CNN. The difference between the proposed model and RobustSTL-CNN in MAPE, MAE, and RMSE metrics is 0.06%, 0.03, and 0.04. Although the difference values are not huge, they are crucial for evaluation of the big dataset taken in this study. The single model of LSTM and GRU basically obtains a good result. However, the hybrid model utilizing decomposition can increase forecasting accuracy, especially in the proposed model. Figure 7 shows the comparison between actual values and predicted values using the proposed model to see the real illustration. The predicted values overall can follow the actual values containing the dynamic pattern and burstiness. The dynamic pattern instance can be seen in the time series data after 21 March 2020. The maximum values of power consumption on 17-21 March 2020 each 24 h always exceeded 37 GWh, but the maximum values in the next days did not reach 37 GWh. The burstiness instance is shown in the red box in Figure 7. There are some points having higher values than common values over a short period. However, the proposed model can overcome the issues above through the predicted values having similarities with the real values. Figure 6: Evaluation results of the proposed model and counterpart models. Moreover, we also present the evaluation using another perspective, i.e., the classification method. After we obtained the clustering result between the actual data and predicted data, we grouped them into nine clusters. The confusion matrix is used to evaluate the experimental result, and the confusion matrix of the proposed model's prediction results in each instance is shown in Figure 8. We can see that the total of TP values is 619, or 86% of all the data.