In this work, we employ the recent envelope estimation method to fit a multiple linear
regression model in order to make predictions of solar irradiance at horizons of 10, 20 and 30
min ahead. The advantage of using this method lies in the fact that there is a reduction in the
variance of the estimated coefficients. Such a reduction is obtained by enveloping the material
part, while excluding the variation of the immaterial part, which also contributes to a reduction
in dimensionality, since there is a decrease in the estimation variance without an increase in the
number of observations. The model was used in a dataset formed by LDR luminosity signals
and statistical attributes extracted from images of the sky. The integration of the LDRs and
the camera was done using a Raspberry Pi 3. Its performance was compared to that of linear
(LASSO and Ridge) and non-linear (Multi-Layer Perceptron) models, being evaluated by the
metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Root Mean Square
Error Relative (rRMSE).