## 3 Methods
### Calculation Process: Number of Iterations
From Tables 1 and 2, it can be concluded that for the heating season, the energy consumption increases when the air changes increase. At the same time, the increment of outdoor temperature implies a reduction of the heating energy. In the cooling season, the energy consumption decreases at the same time that air changes increase. Moreover, it can be observed that energy consumption will increase at the same time that outdoor temperature increases. Despite this, the cooling energy consumption is, overall, reduced for that climatic region. Therefore, it might be interesting to consider changing part of the winter teaching period to the summer season due to the high energy saving and reduced possibility of contagion. As the aim of this work is to define the optimal moment and the exact increment of the number of air changes to lower energy consumption, this will be solved numerically and by the proposed Weibull model constants. To solve it numerically, two models were obtained by curve fitting from the information shown in Tables 1 and 2. The first model, Equation (3), is for the heating period (HE) and was obtained with a determination factor of 0.95:  
\[\mathrm{HE}=15.7-24.9\cdot\mathrm{ACH}-9.6\cdot\mathrm{\Delta t}+27.8\cdot \mathrm{ACH}^{2}+11.1\cdot\mathrm{\Delta t}^{2}-7.38\cdot\mathrm{ACH}\cdot \mathrm{\Delta t} \tag{3}\]  
where \(\mathrm{ACH}\) refers to the air changes (h\({}^{-1}\)) and \(\mathrm{\Delta t}\) is the increase in temperature with regards to the seasonal average value (\({}^{\circ}\)C). When the minimum of this function was solved numerically, a minimum energy consumption of 6.20 kWh/m\({}^{2}\) was obtained during the winter when the air changes are 0.52 h\({}^{-1}\) and there is an increment of 0.60 \({}^{\circ}\)C with respect to the average seasonal value. The maximum of this function, with 77 kWh/m\({}^{2}\), was obtained at the average winter temperature and the number of air changes is 2 ach. This maximum energy consumption corresponds with the proposed option to prevent COVID infections during the winter season. As a consequence, since the objective is to increase the number of air changes, the same analysis was done in the summer season to define the cooling energy (CE) model, defined by Equation (4). This model was obtained with a determination factor of 88.15:  
\[\mathrm{CE}=29.35-35.41\cdot\mathrm{ACH}+1.24\cdot\mathrm{\Delta t}+10.56\cdot \mathrm{ACH}^{2}-0.68\cdot\mathrm{\Delta t}^{2}+0.62\cdot\mathrm{ACH}\cdot \mathrm{\Delta} \tag{4}\]  
where \(\mathrm{ACH}\) refers to the air changes (h\({}^{-1}\)) and \(\mathrm{\Delta t}\) is the increase in temperature with regards to the seasonal average value (\({}^{\circ}\)C). Based on this cooling model, the minimum energy consumption is identified with an increment of 0.15 \({}^{\circ}\)C of the seasonal average temperature and with 1.67 ach air changes. As a consequence, this is the better solution for a compatible increment of air changes in accordance with COVID requirements and a lower energy consumption. The previous resolution is just a numerical resolution of the problem, but it does not let researchers understand the reason why this is the optimal ventilation period compared to alternative options. As a consequence, an analysis of the Weibull model constants was done. From Tables 1 and 2, it can be observed that most of the determination factors present a high value, showing a good agreement between the Weibull model and the probability of energy consumption. In this sense, it can be observed that the constants \"\(e_{0}\)\" and \"\(e_{1}\)\" (shape parameter) can be related with each type of curve and, as a consequence, its corresponding energy consumption. In particular, when the \"\(e\)\" constant reaches a zero value, the curve is exponential and, when it is near to 3.5, a normal distribution is expected and the energy consumption is at a minimum. This is due to that the area below the curve is smaller in a normal distribution with respect to an exponential or a lognormal curve.