ADDF: Multi-Step Load Interval Forecasting for Sustainable Power Systems

The transition toward sustainable power systems requires load forecasting methods that can support renewable integration under increasing uncertainty. However, many deep learning models mix historical load, temporal priors, and external drivers in black-box structures, and often assume that true future driver values are available. To address these issues, this study proposes ADDF (Automatic Driver Discovery and Fusion), a semi-explicit self-driven framework for multi-step load interval forecasting. ADDF organizes historical load, calendar priors, and external drivers into three functional branches to distinguish load inertia, temporal regularity, and external forcing. The Driver Branch estimates future driver states under practical information constraints and uses dynamic gating to screen useful driving information. The three branch representations are adaptively integrated through Three-Way Fusion, followed by bounded residual correction to generate multi-step quantile forecasts. Experiments on the Panama electricity load dataset and ETTh1 dataset under one-step and 24-step settings show that ADDF achieves competitive point accuracy and interval prediction performance. Mechanism analyses indicate that the proposed branch-level structure provides clearer interpretability than post-hoc black-box explanations. The framework offers uncertainty-aware forecasting support for sustainable power system operation, including day-ahead scheduling, reserve planning, and energy management.