Sensor-Independent One-Hour-Ahead Forecasting and Anomaly Detection of Grid-Connected PV Inverters Using an Interpretable Random Forest Framework

Dependable tracking of grid-linked photovoltaic (PV) systems is also unfeasible because it relies on meteorological sensors and rule-based management, especially in sensorlimited situations. The study constructs a machine learning model interpretable to humans based on electrical measurements of a PV plant of 30 kW and 40 kW inverters, and a five-minute resolution dataset of active and reactive power, phase voltages and phase currents and time dependent features during January 2025. An application of the regression, classification, and Z-score anomaly detection method was conducted using the Random Forest and conditioned on the timestamp alignment, feature engineering, percentile-based categorization of the output, and a one-hour-ahead label shifting, and the method was validated with the aid of the temporal splits and 5-fold Time Series cross-validation. The regression model was found to have great predictive accuracy (MAE = 0.12 kW, R2 = 0.995), and cross-validation performed showed great robustness (R2 = 0.9802, MAE = 0.1024 kW). The Z-score analysis (z = 3) revealed the presence of anomalous samples (2.77%). Though the accuracy of a static classification was 86 percent, time-varying forecasting lowered the accuracy to 33 % ( macro F1 = 0.33) which points to the impact of dynamic environmental variability. The suggested light and interpretable structure allows predicting the performance of inverters in real-time, early detecting anomalies, and intelligent planning of PV systems maintenance without the use of external meteorological devices.