## 2 Related Work or Literature Survey
Therefore, it has been deduced that models that can grasp behavioral patterns are also crucial. For example, authors of [36] have taken a detailed look at their regional residential electricity consumption profiles, taking into account household income, wealth, and some demographics. Another research [37] has considered the demographics under a global comparison and provides policy implications. They have both indicated the importance of the data beyond the first years of the pandemic. Besides the residential profile and patterns, some studies have analyzed the impact of COVID-19 on the demand profile of commercial buildings in their region [38]. Another recent commercial building-focused research has been presented by [39], but this study has used data collected from three specific buildings in 15-minute intervals instead of overall consumption. In summary, it is clear that the analysis of data from different settlement zones, buildings as well as regions will help shape the energy policies better, making each study important on a macro level. As stated earlier, very few papers like the hospital case studies of [9; 10] have used on-site data to analyze the impact of the pandemic on the electrical energy demand. There exist some university campus energy-management projects utilizing on-site data, such as [40; 41; 42], but in general, publicly available electricity consumption data have been used. Besides that, the main difference between the aforementioned papers in this literature review was their methodical approaches. A detailed outlook is provided in Table 1, where reference works are compared based on forecast models (performed and benchmark), application types, and horizons. The word performed model means the model with the best result. In some cases, these are the researchers' proposed models. Please note that some of the cited works are not included in the table. This is because a couple of them contain compiled results from several reviewed papers, which would be too large to fit, and some of them do not use forecasting methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Application** & **Temporal** & **Forecast** & **Performed** & **Benchmark** & **Performance** \\  & **Type** & **Granularity** & **Horizon** & **Model** & **Models** & **Results** \\ \hline
[9] & Healthcare & Monthly & Monthly & MLR & MLR & 0.044 \\  & Facil. & Monthly & Yearly & ANN & ANN & 1.459 \\
[10] & Hospital & Monthly & Yearly & ANN & & (MAE) \\
[17] & Indust. & Compl. & Hourly & Monthly & DT & GLM, DL, RF, & 6.900 \\  & Campus & & & & GBT, SVM & (RE) \\
[40] & Compsl. & 15-min & Monthly & FBP & FBP & 11.100\% \\
[41] & Microgrid & 15-min & Daily & QRF & QRF & 0.223 \\
[39] & Cml. & Buildings & 15-min & Daily & Robust- & mv-M-LSTM- & 0.024\% \\  & & & & & CI & (nRMSE) \\  & & & & & adv-M-LSTM, & \\  & & & & & adv-LR, & \\  & & & & & adv-LSTM, & \\
[32] & Natl. Grid & Daily & Daily & DeepAR & RNN, FFNN, & 0.050\% \\  & (Vars. & & & Seasonal & & (WAPE) \\  & Countries) & & & & Näive & ETS \\ \hline \end{tabular}
\end{table} 
Table 1: Details of studies regarding the impact of COVID-19 on electrical energy consumption forecasting. Table 1 highlights the lack of hospital case studies among the related works. Compared to [9; 10], the data collected for this research belongs to the entire active period of the pandemic. The temporal granularity is 15 min, and the forecasting was implemented