A Decision-Analytics Framework Combining DEA, SFA, and ML with Mixture Optimization for Industrial Photovoltaics

The rapid rollout of industrial photovoltaic (PV) generation demands robust tools for benchmarking performance and guiding investment. Traditional efficiency techniques-non-parametric data envelopment analysis (DEA) and parametric stochastic-frontier analysis (SFA)-offer complementary strengths, yet each has recognized limitations when applied in isolation. This study proposes a hybrid framework that i) estimates multiple DEA and SFA frontiers; ii) learns their predictive patterns with Random Forest, Gradient Boosting and XGBoost models; and iii) integrates the resulting efficiency scores through an optimized weighting scheme derived from a simplex-lattice mixture design of experiments. The approach is demonstrated on a statewide dataset covering 70 immediate geographic regions of Minas Gerais, Brazil, comprising installed capacity, number of PV units, solar-radiation estimates and realized energy output. Optimization yields a parsimonious composite based primarily on the DEA and SFA models, reducing the principal-component loss metric by 18 % relative to equal weighting. Spatial analysis reveals a clear north-south efficiency gradient correlated with solar insolation; nonetheless, centrally located industrial hubs also achieve high scores, underscoring the moderating role of infrastructure. The findings highlight regions where policy support or technology upgrades could deliver the greatest marginal gains and illustrate how mixture-design optimization can reconcile diverse frontier estimates, offering a generalizable decision-analytics toolkit for renewable-energy assessment.