*3.2. Correlation of Database Values with the Exterior Influence Factors*

We used a standard correlation model for a yearly database [2]:

$$\tau = \text{Correl}(\mathbf{x}, y) = \frac{\sum\_{0}^{i=8760} (\mathbf{x}\_i - \overline{\mathbf{x}})(y\_i - \overline{\mathbf{y}})}{\sqrt{\sum\_{0}^{i=8760} (\mathbf{x}\_i - \overline{\mathbf{x}})^2 (y\_i - \overline{\mathbf{y}})^2}} \tag{2}$$

where *x* is the actual power database value and *x* is the average of similar temporal values, e.g., same time interval of the same day of the week in the same season; *y* is the actual meteorological/daylight database value and *y* is the average of similar temporal values.

## *3.3. Linear Regression*

The first forecasting in each line of estimation was carried out with the industry's most commonly used method [20]:

$$\hat{g}(t) = a\_0 + \sum\_{i=1}^{n} a\_i \mathbf{x}\_i(t) + r(t) \tag{3}$$

where:

*y(t)* is value at time *t* to be forecasted; *x1(t)* represents the influence factors; *r(t)* is the residual load at *t*; *ai* is the regression parameter.
