Data‐Driven Prediction of Photovoltaic Inverter Temperature Using Operational Dashboard Data: A Comparative Machine Learning Study

Effective thermal management is crucial to PV inverter reliability and lifetime, as power electronic components are highly sensitive to thermal stress. However, many existing monitoring methods rely on complex simulations or costly experiments. This study proposes a data‐driven approach to predict inverter temperature using standard variables from online monitoring dashboards. Five machine‐learning models, such as decision tree, random forest, gradient boosting, histogram‐based gradient boosting, and XGBoost, were evaluated; random forest performed best on an independent test set (R 2 = 0.9947, RMSE = 0.601°C, MAE = 0.471°C). SHAP analysis further provided interpretability and revealed a clear inverse relationship between reactive power and inverter temperature, alongside the effects of the daily cycle and power output. Overall, subdegree (< 1°C) accuracy was achieved with only seven input features, enabling a practical, cost‐effective solution for predictive maintenance and real‐time monitoring.