Prototype-routed exponential-gated residual learning for multi-horizon regional electricity-load forecasting
Short-term electricity-load forecasting (STLF) supports reliable dispatch, reserve scheduling, renewable integration, demand-response planning, and low-carbon power-system operation. Existing forecasting models often struggle to jointly capture heterogeneous load-weather regimes, horizon-dependent residual errors, and causal reliability control for multi-horizon prediction. To address this gap, this study proposes a Prototype-Routed eXponential-Gated Residual Network (PRX-Net) for six-hour-ahead regional electricity-load forecasting. PRX-Net combines a prototype-routed randomized backbone, recurrent residual correction, and causal previous-error gating. The randomized backbone uses global and local enhanced edRVFL experts to model broad and regime-specific load-weather behavior, while the recurrent module estimates the remaining horizon-dependent residual. The exponential gate controls the residual contribution using matured error evidence from previous forecast origins. Experiments on three regional load-weather datasets show that PRX-Net achieves root-mean-square error (RMSE) =740.82, mean absolute error (MAE) =528.89, mean absolute percentage error (MAPE) =2.02%, R2=0.9808, normalized RMSE (NRMSE) =2.90%, and normalized MAE (NMAE) =2.09%. Compared with edRVFL, iTransformer, and PatchTST, PRX-Net reduces average RMSE by 12.91%, 2.49%, and 11.69%, respectively. These results show that combining regime-aware randomized forecasting with gated residual correction improves multi-horizon regional load prediction. © 2026 Elsevier Ltd.
Библиографическая ссылка
Haq, Izaz Ul, Liu, Fang, Sidorov D.N., Zhu, Jiran, Huang, Suzhen, Aslam, Laeeq Prototype-routed exponential-gated residual learning for multi-horizon regional electricity-load forecasting // Computers and Electrical Engineering. Vol.140. No.111557. 2026. DOI: 10.1016/j.compeleceng.2026.111557
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