## 1 Introduction  
The COVID-19 outbreak quickly began to spread around the world and was classified as a pandemic by the World Health Organization (WHO) in March 2020 [1]. After more than four years, despite the lack of agreement by experts [2] or ordinary people of daily life, the WHO has not officially called back the pandemic status of COVID-19 [1]. According to the WHO database, 777,126,421 total cumulative cases were reported worldwide just a week before the end of 2024 [3]. Thanks to the vaccination efforts, the number of deaths has reduced, but COVID-19 is still a very dangerous disease whose short- and long-term health effects have not yet been fully understood. The pandemic has affected every aspect of life, including the energy sector [4]. The pandemic measures reduced the overall demand for electricity worldwide [5]. The residential demand largely increased, whereas the industrial and commercial demand significantly reduced. The reduction in the latter outweighed the increase in the first. Thus, the power mix was altered [6]. However, statistics [7] from later stages of the pandemic show that, due to the relaxation of COVID-19 measures, the energy demand saw a resurgence, surpassing pre-pandemic demand levels. The trends above verify the complexity of the problem. Understanding the pandemic to the fullest extent is crucial for designing a resilient and sustainable future since future pandemics might be inevitable [8]. Calling in a responsive healthcare system, researching microorganisms, and redesigning energy policies alone may not be enough. After all, it is a complex optimization problem with many layers. One of the many remedies is to collect as much data as possible. Despite the interest and the impressive amount of high-quality research, the majority of energy forecasting-related studies in the literature use data on macro levels. There are not enough contributions that narrowed down their research to more micro levels, i.e., sector-focused or building-focused applications (see Section 2 for more detail). Hospitals/Healthcare facilities formed the frontline in the struggle against COVID-19. For example, a hospital with the size and power of the one analyzed in this study is an industrial behemoth. Strategically, it is vital for such a healthcare complex's energy management to be efficient and resilient versus unpredictable consumption patterns. Demand forecasting, therefore, may help the building adjust itself and adapt to changing levels of consumption even in the face of an event like the pandemic, thus creating the primary motivation behind this paper. The novelties and contributions of this research may sum up as follows:  
* The first research using DNN and GEP methods among the related works (see Section 2). * The first study in Turkiye that uses actual on-site data to investigate the impact of COVID-19 on electrical energy demand. In addition, there has not yet been another research study in Turkiye with hospitals/healthcare facilities as its application domain. * One of the very few works among [9; 10] around the world investigating the impact of the pandemic on the energy demand of hospitals/healthcare facilities by utilizing on-site data. * First study that incorporates the electricity consumption, weather, and COVID-19 data all together in the forecasting equations. * The data have been collected over the entire active stage of the pandemic (March 2020-June 2022). Many related works in the literature only cover a portion of this time frame. The remainder of this paper is organized in the following order. Section 2 covers a literature survey to investigate the impact of COVID-19 on the electricity demand. The similarities and differences between these works are discussed and illustrated in a table. Section 3 consists of the material and methods. The material explains how the data were collected and wrangled. It defines the input variables and their correlations. The section ends with a description of the methods that are used for the data analysis, namely DNN and GEP.