*4.1. Descriptive Statistics of the Analyzed Variables*

The values of descriptive statistics show the character of the empirical distribution of the analyzed variables. Table 1 presents descriptive statistics as well as measures of dispersion and shape of distribution of the analyzed variables in the pre-pandemic period (Table 1) and during the COVID-19 pandemic (Table 2).

In the pre-pandemic period, the variables show a relatively high similarity of empirical distributions to the normal distribution. In the case of variables H2, R4 and R6, the values of descriptive statistics and the parameters of the shape of empirical distributions of these variables prove their significant normality. This is confirmed by the graphical analysis of the histograms (Figures 13–16).

During the pandemic, in research objects of C1, C2, H3, R1, R2, R3, R4 and R5 cases, the average value of hourly electricity consumption is higher than the median, and therefore more observations are on the left-hand side of the average value, which indicates the righthand value asymmetry of the empirical distribution. The concentration of the empirical distribution (kurtosis) for only three cases of H4, R6 and LPS is below 3, which means that they are platocurtic distributions, and the values of the variable are less concentrated than with the normal distribution. In the case of six variables, it is close to the 3 value typical for the normal distribution. The cases of the H6 and LPS variables show the greatest similarity to the normal distribution, but their empirical distributions, intuitively, do not meet the normality criteria.

In the case of insolation conditions in the location of the analyzed case studies during a pandemic, the distribution of the variable is of course similar to the pre-pandemic period, due to the numerous occurrences of zero and close to zero values, it shows a strong lefthand asymmetry. Literature sources confirm that the empirical distribution of horizontal insolation does not show similarity to the normal distribution and is best approximated by the beta distribution [56–58]. Sources also indicate that for some seasons and latitudes, the empirical distribution may approximate the Weibull distribution [59,60] commonly used for wind speed analysis, as well as the log-normal [61] or gamma [58,61] distribution.

Therefore, the use of the Pearson linear correlation method for all variables may be associated with the incorrect determination of the value of the correlation coefficient and lead to erroneous conclusions. Therefore, the nonlinear rho-Spearman correlation method was used as an alternative, although less intuitive in interpretation.
