## 5 Conclusions  
This research has presented hour-ahead and day-ahead electricity demand forecasting using DNN and GEP methods to predict the electricity consumption of a university hospital based on the data collected during the COVID-19 pandemic. The hospital case study incorporates multiple aspects, such as electricity consumption (data retrieved on-site), weather, and COVID-19 data, into the forecasting models, making the research unique among its counterparts. The DNN has proven to be the superior choice of algorithm for the predictions with lower nRMSE values compared to that of the GEP. The prediction errors for the DNN were observed to become as low as 5.66% and 11.13% for the 1 h-ahead and 24 h-ahead models, respectively, whereas the GEP returned 6.17% and 12.99%. These amount to significant improvements in prediction accuracy by 8.27% and 14.32%. The DNN has also performed more efficiently with much shorter computational times. It completed the calculations in 30.70 s and 34.81 s, while the GEP finished in 178.75 s and 162.33 s for the hour-ahead and day-ahead cases, respectively. These results amount to significant improvements of 82.33% and 78.56%. As a result, the impact of COVID-19 on the electricity consumption of the hospital was observed to be dependent on the combination of daily new cases and restriction status (see Figure 2). For example, when the daily new cases were escalating rapidly and so a lockdown was implemented shortly, abrupt declines in the electricity demand were recorded. On the other hand, when the case numbers were high but decreasing, and thus the lockdown was lifted, abrupt rises in the demand were observed. These sharp changes were usually within the band of 0.5 MWh, more or less. To summarize the paper, the objectives and motivations of this research can be reiterated as:  
* The main objective was to predict the electricity demand of the hospital during the COVID-19 pandemic and investigate its impact on the building's electricity consumption, motivated by the critical role of the healthcare systems,
* To collect a substantial amount of data to enhance the strength of the analysis,
* Offering a novel methodological approach and verifying the performance of the DNN and GEP methods in demand forecasting,
* To discuss the implications and findings of the study and compare them with the literature. The outcomes of the research, including the novelties and contributions, are given by:  
* This research took a novel approach in augmenting the electricity consumption, meteorological and COVID-19 data (as shown in Table 2) within the equations used in predictions. The effects and relationship of these parameters were expressed as unique model equations (refer to Tables 3 and 5). The DNN and GEP algorithms, which were used for the first time in a COVID-19-based forecasting study, showed significant performance using these forecasting models to provide efficient predictions. * The data retrieved covered a larger period than many other contributions in the literature (the whole active period of the pandemic: March 2020-June 2022). * The impact of the pandemic on the electricity consumption of the hospital was observed to depend on the daily new cases and restriction measures alternating between sharp increases and declines in consumption, but as expected in such studies, the greater impact was due to the meteorological data because the AC usage is naturally accepted as the major contributor to consumptions. * Among the COVID-19-related forecasting literature, this research is the second to utilize on-site data of a hospital and the first of its kind from Turkiye. Future works can cover the extension of this research from the Eastern Mediterranean to the rest of the world for electricity demand forecasting in hospitals or healthcare facilities during extreme events. As stated earlier, the rising portion of RES creates a mismatch in demand and production of electrical energy due to their intermittent nature, which proves to be a significant challenge for the stability of grids.