EnergyCA-LLM: Context-Guided Window-Adaptive Residual Learning for Unified Renewable Generation and Load Forecasting

Growing renewable penetration is broadening power-system forecasting from isolated load prediction to unified forecasting of electricity demand, wind power, photovoltaic generation, and net load. These tasks share temporal structure but differ in physical drivers, operating regimes, and forecast-origin information availability. Existing methods study them separately or use static, task-agnostic context, limiting dynamic specialization. We propose EnergyCA-LLM, the first LLM-guided framework to jointly forecast electricity demand, wind power, photovoltaic generation, and net load within a single shared numerical backbone through forecast-origin-aware, window-specific residual adaptation. Per window, an availability-aware schema organizes the forecast-origin information set, covering task semantics, asset and geographic attributes, calendar and weather information, recent operating states, and field availability. A frozen pretrained LLM encodes this schema into a shared semantic state that interacts with the numerical latent state to produce residual codes. These codes gate multilayer low-rank residual adapters for task- and regime-specific modulation of shared temporal representations. Across four public benchmarks, chronological evaluation with four complementary point-forecasting metrics shows strong and competitive results against task-specific energy, general numerical, and LLM-enhanced baselines. Unified-training, encoder-replacement, and adapter-control ablations assess the roles of parameter sharing, pretrained semantic encoding, and dynamic residual adaptation.