# Forecasting of Power Demands Using Deep Learning
## 3 Results and Discussions  
In this paper, we used mean absolute error (MAE) and R\({}^{2}\) in order to evaluate the performance of the proposed models. These parameters were calculated using the Scikit-learn tool ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/))). We predicted future demands of facility capacity, supply capacity, and power consumption. We tested the three developed tools namely CNN, RNN, and the hybrid model using different values of histories and futures. Table 2 shows the best performing model with its past and future configurations for the three features of the study. The '-' sign in Table 2 means that the model did not fit the data at all. CNN model significantly outperforms RNN and hybrid models. We have extensively searched for the best hyperparameters and architectures of RNN models but these models did not converge. The main reason is that the size of the training dataset is not large enough to train the RNN model. Thus, our future work will concentrate on collecting more data from Korean Power Exchange to train more accurate forecasting models. Therefore, we will consider the CNN model for power demands forecasting. Figure 6 shows the heat maps of the R\({}^{2}\) results for all past against future configurations in the CNN model. For facility capacity forecasting, we can see that the CNN model performs outstandingly for all combinations. The best R\({}^{2}\) value is 0.992 for the past of 7 days and the future of 1 day. On the other hand the minimum R\({}^{2}\) is 0.820 for the past of 7 days and the future of 15 days. Thus, we can use the developed CNN model for facility capacity forecasting using different past values for predicting different future values. For supply capacity forecasting, we can see that the CNN model performs well for some combinations only, forecasting up to 15 days. The best R\({}^{2}\) value is 0.851 for the past of 7 days and the future of 1 day. Thus, we can use the developed model for short-term forecasting. The model can forecast with 0.69 of R\({}^{2}\) for the past of 7 days and the future of 7 days. Furthermore, the R\({}^{2}\) is 0.43 for the past of 7 days and the future of 15 days. For power consumption forecasting, we can see that the CNN model performs well for some combinations only. The best R\({}^{2}\) value is 0.772 for the past of 60 days and the future of 1 day. Thus, we can use the developed model for short-term forecasting. The model can forecast with 0.68 of R\({}^{2}\) for the past of 45 days and the future of 7 days. Furthermore, the R\({}^{2}\) is 0.59 for the past of 45 or 60 days and the future of 15 days. Furthermore, the MAE of the CNN model outperforms the MAE results of the other models. For instance, the MAE of facility capacity feature is 0.025. On other hand, the MAE of the supply capacity of the CNN model is better by 0.265 than the hybrid model. Similarly, the MAE of the CNN model in power consumption features is better by 0.095 of the hybrid model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Model** & **Past (days)** & **Future (days)** & **R\({}^{2}\)** & **MAE** \\ \hline Facility Capacity (MW) & CNN & 7 & 1 & 0.992 & 0.025 \\ Supply Capacity (MW) & & 7 & 1 & 0.851 & 0.214 \\ Power Consumption (MW) & & 60 & 1 & 0.772 & 0.314 \\ \hline Facility Capacity (MW) & RNN & - & - & - & - \\ Supply Capacity (MW) & & - & - & - & - \\ Power Consumption (MW) & & - & - & - & - \\ \hline Facility Capacity (MW) & Hybrid (CNN+RNN) & - & - & - & - \\ Supply Capacity (MW) & & 60 & 1 & 0.336 & 0.479 \\ Power Consumption (MW) & & 7 & 1 & 0.627 & 0.409 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The best performance of the proposed models with different past and future configurations. In order to see the real performance of the proposed model, we compared it with a support vector machine (SVM) and artificial neural networks (ANN).