Toward robust household load forecasting in smart grids using uncertainty-aware and cross-regional hybrid deep learning
Accurate and transferable household (HH) load forecasting is essential for the stable and real-time operation of renewable-energy-integrated smart grids; however, forecasting models trained on region-specific data often exhibit limited generalization across heterogeneous climatic and behavioral domains. To address this challenge, this study proposes a Cross-Regional Uncertainty-Aware Hybrid (CRUA-Hybrid) deep-learning (DL) framework that unifies dual dynamic feature gating, hybrid temporal representation learning, Transformer-based attention fusion, and adversarial domain adaptation for robust and transferable household load prediction. Specifically, a Dynamic Feature Gating (DFG) mechanism performs dual temporal and channel-wise gating to selectively emphasize salient information from input load sequences. A hybrid encoder combining Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) branches captures both short-term and long-term temporal dependencies, while a Transformer-based Cross-Model Attention Fusion (CMAF) module enhances the global contextualization of multi-branch representations. In addition, a Gradient Reversal Layer (GRL) enables adversarial domain adaptation between the Germany source domain and the Morocco target domain to improve cross-regional transferability. Experimental results show that, compared with the strongest baseline methods, the proposed model achieves performance improvements of 69.8% in RMSE, 15.1% in sMAPE, 26.6% in PMAE, 4.5% in Peak-MAE, 62.5% in CRPS, and 1.4% in PICP, indicating improved forecasting accuracy, peak prediction performance, and uncertainty calibration and strong adaptation to unseen HH and regions in renewable-energy-enabled smart grid environments. © 2026 Elsevier Ltd.
Библиографическая ссылка
Ullah, Shafqat¶, Liguo, Wang¶, Amin, Sareer Ul¶, Khan, Aftab Alam, Dreglea, Aliona, Sami, Irfan, Sidorov D.N. Toward robust household load forecasting in smart grids using uncertainty-aware and cross-regional hybrid deep learning // Computers and Electrical Engineering. Vol.138. No.111319. 2026. DOI: 10.1016/j.compeleceng.2026.111319
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