Adaptive smoothing drift normalization for day-ahead net load forecasting in renewable power system

The increasing integration of renewable energy sources introduces significant variability and non-stationarity into power system, challenging accurate net load forecasting. Although net load forecasting research has devoted considerable efforts to handle non-stationarity – via normalization, incremental learning, or drift detection – existing solutions often suffer from hyperparameter tuning, threshold-based triggers, or reliance on specialized architectures. To overcome these limitations, we propose Adaptive Smoothing Drift Normalization (ASDN), a lightweight normalization layer that continuously adapts to distribution shifts without threshold tuning. ASDN effectively adapts to new data via a mechanism that combines entropy-based adjustments with a dynamic filtering approach. At the same time, it maintains stability with respect to historical patterns, allowing the method to capture both gradual and abrupt shifts in the data distribution. We provide a theoretical guarantee that the estimation error of ASDN remains bounded under piecewise-stationary drift; as incremental drift and noise decrease, this bound tightens and converges to zero. Experiments on nine forecasting models across five public datasets and four prediction horizons show that ASDN consistently outperforms traditional normalization techniques, reducing mean squared error and enhancing robustness. These results confirm ASDN’s effectiveness in handling complex temporal dynamics, making it valuable for improving forecast accuracy in dynamic renewable power systems.