## 4 Results and Discussion
### Discussion  
Predictive model equations of buildings' electricity demand are accustomed to employing a variety of exogenous variables such as historical electricity demand, temperature, and, accordingly, short-wave irradiation along with calendar variables [43]. It is the first time in the literature that this paper has attempted to introduce the significant quantities of a pandemic like COVID-19 to forecast an hour-ahead and day-ahead electricity demand of a large building complex serving as a university hospital. For the hour-ahead forecasts, it is revealed that both of DNN and GEP model equations utilize the number of daily new cases to forecast the electricity demand. In addition, the hour-ahead DNN model used the number of cumulative cases, lockdown, and full lockdown restrictions. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**DNN**} & \multicolumn{3}{c}{**GEP**} \\ \cline{2-10}  & \(\mathbf{R^{2}}\) & **nMAE** & **nRMSE** & **Run Time** & \(\mathbf{R^{2}}\) & **nMAE** & **nRMSE** & **Run Time** \\
**Performance** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
24 h-Ahead Model & 93.39 & 7.39 & 11.13 & 34.81 & 91.00 & 8.03 & 12.99 & 162.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performances of the proposed models for day-ahead electricity demand forecasting. Figure 8: Expression tree of the best GEP model for day-ahead electricity demand forecasting. Furthermore, the predicted day-ahead forecasts unveiled that DNN and GEP model equations both processed the full lockdown restriction in forecasting the electricity demand. Moreover, the day-ahead DNN model deployed the number of new cases, while the GEP model included the curfew-restriction parameter. In comparison, the two hospital/healthcare application case studies that were previously discussed [9; 10] use MLR and ANN methods, respectively. The MLR is a simple approach but prone to severe multicollinearity, and the ANN is quite a commonly used method with tendencies to overfitting. Furthermore, the resolution of the forecasting in this research is 15 min, which is much higher than the studies in comparison here. The forecast horizon is shorter and covers both hourly and daily predictions, adding to the novelty. Consequently, it is considered that the effects of the aforementioned COVID-19 parameters in the model equations are unique and vital findings and also original contributions to the building electricity demand forecasting literature in the very short-term and short-term horizon not only for COVID-19 pandemics but also for forthcoming unprecedented events. For further discussion, Table 7 presents the individual error metrics over the seasons of each year the data set covers. This is also graphically represented in Figure 9. They reveal that the minimum errors happened during the summer peak times, in August of each year. On the other hand, the maximum errors generally occurred towards the end of a year. Figure 10 demonstrates the performance of DNN and GEP during the hourly and daily active power peaks that occurred within a selected week when the consumption was the highest throughout the whole data set. The actual electricity consumption is compared to the predictions made by the DNN and GEP methods. The illustration verifies the results presented in Sections 4.1 and 4.2 and highlights the effectiveness of the methods. Figure 9: Illustration of the seasonal error metrics comparison of DNN and GEP.