This study aims to develop a predictive hybrid model for a grid-connected PV system with DC-DC optimizers, designed to operate in extreme altitude conditions at 3800 m above sea level. This approach seeks to address the “curse of dimensionality” by reducing model complexity and improving its accuracy by combining the recursive feature removal (RFE) method with advanced regularization techniques, such as Lasso, Ridge, and Bayesian Ridge. The research used a photovoltaic system composed of monocrystalline modules, DC-DC optimizers and a 3000 W inverter. The data obtained from the system were divided into training and test sets, where RFE identified the most relevant variables, eliminating the reactive power of AC. Subsequently, the three regularization models were trained with these selected variables and evaluated using metrics such as precision, mean absolute error, mean square error and coefficient of determination. The results showed that RFE – Bayesian Ridge obtained the highest accuracy (0.999935), followed by RFE – Ridge, while RFE – Lasso had a slightly lower performance and also obtained an exceptionally low MASE (0.0034 for Bayesian and Ridge, compared to 0.0065 for Lasso).
Pour en savoir plus : Predictive hybrid model of a grid-connected photovoltaic system with DC-DC converters under extreme altitude conditions at 3800 meters above sea level