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• Performance assessment and production estimation of grid-connected PV station. • Strong relationship between weather variables and the output of the PV station. • Strong interdependence of the meteorological parameters and performance parameters. • The distinction between the different variable correlation results is a hard task. • Feature selection and PCA analysis were used as dimensional reduction techniques. • Dimensionality reduced data was used for training random forest models. Meteorological variables have an important effect on the performance of a grid-connected photovoltaic station, in this paper, the impact of meteorological variables on the 6 mWp grid-connected photovoltaic station in the desert of Adrar has been explored through performance assessment and output power forecasting. The impact of the meteorological variables on performance parameters has been investigated by performing interdependence and correlation studies. A clear interdependence between some variables has been observed, but the complete separation between each variable correlation effect has proven to be a difficult task. A combination of random forest method and pre-processing techniques namely feature selection and Principal component analysis has been developed in order to predict power production using meteorological variables as inputs. The forecasting models have been evaluated in terms of computation time, accuracy, and several statistical indicators. [ABSTRACT FROM AUTHOR]