To this aim, ten physics-based models have been investigated to determine the cell temperature, and those models have been validated using measured PV cell temperatures by computing the Root Mean Square Error (RMSE). Then, the model with the lowest RMSE has been adopted in training a data-driven prediction model. The proposed prediction model is to use an ANN compared to the well-known benchmark model from the literature, ., Multiple Linear Regression (MLR). The results obtained, using standard performance metrics, have displayed the importance of considering the cell temperature when predicting the PV power output. | Analysis of the effects of cell temperature on the predictability of the solar photovoltaic power production
