Condition monitoring and forecasting applications require accurate PV models that can predict power from weather parameters. However PV output is also dependent on the potentially suboptimal behavior of the MPPT controller, which can introduce both inefficiencies and prediction challenges. In this work, we use a data-driven approach to show that MPPT controllers do not always operate at the optimal knee point of the I-V curve and propose methods to quantify these inefficiencies. Based on these findings, we develop novel machine learning PV models that predict current and voltage separately and capture the behavior of the MPPT system more accurately. We present evaluation results using data collected from a large solar farm, which shows that the proposed models can reduce estimation errors significantly as compared to state of the art methods.