Modelling No-Load Losses in Distribution Transformers: A Comparison Between Physical and Machine Learning Approaches

Although numerical simulations can provide high accuracy predictions, they are computationally expensive and time-consuming, which limits their use in iterative optimization workflows. In this study, a refined physics-based loss-separation model and a data-driven model based on extreme gradient boosting algorithm are developed and validated using a dataset of more than twelve thousand measured transformers operating at 50 Hz. The physical model achieved a mean absolute error (MAE) of 13.7 W with a standard deviation of 22.2 W, while the extreme gradient boosting algorithm (XGBoost) model reduced these values to 9.6 W and 13.0 W, respectively. This corresponds to a reduction in error variance of approximately 40% and an increase in prediction accuracy, with 88.7% of the machine learning (ML) predictions falling within ±20 W of the measured losses, compared to 78.4% for the physical model. Overall, the results demonstrate that the machine learning approach delivers superior predictive precision, while the physics-based formulation remains valuable for interpretability and reliable extrapolation beyond the training domain.