Reducing Phase Lag and Noise in Residential Load Forecasting with a Hybrid Residual-Attention Model

Residential short-term load forecasting (STLF) plays a critical role in maintaining grid stability and enabling effective demand response in smart grid environments. However, residential electricity consumption exhibits strong stochastic variability, non-linear patterns, and abrupt changes caused by occupant behavior and environmental factors, making direct forecasting of raw power signals highly susceptible to high-frequency noise and temporal prediction lag. To address these challenges, this study proposes a Robust Residual Attention Network (Robust-RAN) for trend-oriented residential STLF. The proposed framework integrates localized causal denoising, residual multi-head attention mechanisms, and cyclic temporal encoding to mitigate observable phase lag and stabilize predictions against stochastic fluctuations, capture both short- and long-term temporal dependencies, and preserve daily periodicity. The proposed model was evaluated on the public UCI Appliances Energy Prediction dataset using comprehensive regression, classification, and residual analysis metrics. The experimental results demonstrate that Robust-RAN achieves a Coefficient of Determination (R2) of 0.9063 and a Mean Absolute Error (MAE) of 13.00 W. Under high-load conditions, the proposed approach further attains a peak detection accuracy of 96.63% with an F1-score of 0.8529, indicating reliable prediction of critical demand events. The results demonstrate that the proposed framework provides a stable and accurate estimation of the underlying residential load trend, offering a stable forecasting framework evaluated on the UCI benchmark dataset for demand response, peak- load management, and advanced residential energy management applications.