Modeling of photovoltaic array using random forests technique

Abstract

This paper presents a novel technique for modeling of photovoltaic (PV) array using random forests (RFs). Metrological variables such as solar radiation and ambient temperature as well as actual output current of a 3 kWp PV grid-connected system installed at Universiti Kebangsaan Malaysia have been utilized. These data are used to train and validate the proposed RFs model. Three statistical error values, namely, root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE), are used to evaluate the developed model. The results show that the proposed RFs model accurately predicts the output current of the PV system. The RMSE, MAPE, and MBE values of the RFs model are 2.7482%, 8.7151%, and −2.5772%, respectively.