Forecasting–Scheduling Co-Optimization for Rural Microgrids: An Edge-Deployable Approach

The high penetration of distributed renewable energy in rural microgrids imposes severe physical-layer fluctuations, weak information-layer communication, and limited computing-layer resources. These triple constraints create a fundamental tension: high-precision forecasting and real-time scheduling are required, yet edge devices face severe resource limitations. To resolve this, we present an edge-deployable energy management system (EMS) that achieves forecasting–scheduling co-optimization. We first propose an Adaptive Gated Dual-stream Network (AGDN), which employs a feature-dimension gated fusion mechanism to overcome the limitations of the local dependency strengths of Long Short-Term Memory (LSTM) and the global perception capabilities of Transformer models under volatile rural conditions. This approach achieves a Mean Absolute Percentage Error (MAPE) of 4.2% for load forecasting, outperforming baseline models by a significant margin. Next, we introduce a Prediction Uncertainty-Guided Quantum-Inspired Optimization (PUG-QIO) algorithm that adaptively maps prediction confidence intervals to quantum rotation angles, enabling deep integration of forecasting and scheduling and yielding an energy utilization rate of 93.2%. Finally, a Temporal Sensitivity-Aware Differentiated Pruning (TSADP) strategy is developed to maintain forecasting accuracy under a 63% parameter compression, overcoming the deployment barrier for high-precision models on edge devices. A 30-day field trial confirms that the proposed system meets the stringent rural requirements across four critical dimensions: forecasting accuracy, real-time responsiveness, lightweight architecture, and economic viability. Overall, the proposed system satisfies four key rural requirements: forecasting accuracy (MAPE = 4.2%), real-time response (≤10 s), lightweight deployment (memory < 500 MB), and economic viability (27.3% fuel cost reduction).