Two-phase drift monitoring and automatic retraining (TPDM-AR) for electric load forecasting

Monitoring incremental concept and data drift is important for electric load forecasting in smart buildings. This study proposes TPDM-AR (Two-Phase Drift Monitoring and Automatic Retraining), a customizable workflow that combines Adaptive Windowing (ADWIN), Kolmogorov-Smirnov Windowing (KSWIN), and Page-Hinkley (PH) drift detectors with a multilayer perceptron (MLP) regressor. Phase 1 monitors the raw active-power stream for distribution shifts, while Phase 2 confirms predictive degradation by tracking mean absolute error (MAE) using a smoothed MAE trajectory compared with a baseline reference threshold defined from a reference window. Retraining is initiated only when Phase 2 satisfies a consensus-and-persistence confirmation rule on the forecasting-error stream, while Phase 1 alerts on the raw series are reported as complementary signals. The method is validated on controlled synthetic linear and logarithmic drift scenarios and on the UCI Individual Household Electric Power Consumption dataset, reporting detector alert times and the corresponding evolution of forecasting error under the same monitoring setup.•Two-phase drift monitoring that combines early change alerts with performance confirmation.•Retraining is triggered only after Phase 2 consensus-and-persistence confirmation on MAE.•Validated on synthetic drift and real-world electricity-demand data.