**5. Conclusions**

In this study, we performed feature selection in order to prioritize inputs in the predication of energy consumption in an artificial illumination system for a CPPS using linear and nonlinear regression models. A dataset was generated with electrical measurements for proprieties such as intensity, light wavelength (RGB and W), frequency, and duty cycle.

The algorithms used for the linear models to identify the elimination order of the features included the variance threshold, Pearson correlation, univariate liner F-regression, and sequential backward feature selection with linear regression.

On the other hand, for nonlinear models, the algorithms used were the variance threshold, mutual information gain, and sequential backward feature selection with tree decision regression, using a tree depth from 2–5. The Kruskal–Wallis test served to validate the elimination order distributions.

The best order for eliminating features with the linear model was duty cycle, light color, frequency, and intensity, with *pvalue* = 0.012364. The best order with nonlinear models was white, green, blue, duty cycle, frequency, red, and intensity, with significance at *pvalue* = 0.007161. The elimination order for the duty cycle and R in the linear and nonlinear models differed enormously because the linear algorithms considered them the most suitable elimination features, while nonlinear algorithms marked them as essential features. This discrepancy was because the duty cycle and R were nonlinear features. Thus, only nonlinear models could map them correctly. Moreover, this supports the hypothesis that the energy consumption in LED lamps for CPPSs has nonlinear behavior and that nonlinear models should be used to predict it.

This technique allows various deductions to be drawn from the analysis of the data obtained, including the estimation of the average energy consumption and its comparison with the quality of the crop, as well as the determination of the circumstances under which energy use is efficient. The selection of characteristics can be used as a reference for the agro-industrial community.

**Author Contributions:** All authors conceived the experiments; E.O.-G., N.E.-G. and J.A.D.-A. collected the data for the experiments; M.M.R., N.E.-G., E.O.-G. and P.V.-J. conducted the experiments, performed the statistical analysis, and generated the figures. All authors wrote and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** We acknowledge the support of the Consejo Nacional de Ciencia y Tecnología (CONACYT) in Mexico for supporting this work through funds for the projects INFRA-2016-01, Project No. 270665, and CB-2016-01, Project No. 287818.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
