Quantum-Enhanced Forecasting and Scheduling in Integrated Energy Systems: Hierarchical Framework and Application Guide

Driven by carbon neutrality goals, Integrated Energy Systems (IES) face the coupled challenges of high-dimensional nonlinear forecasting and combinatorial explosion in scheduling. Classical deep learning and optimization methods struggle with complex nonlinearities, large discrete spaces, and tightly coupled constraints. Meanwhile, advances in superconducting and trapped-ion hardware—scaling from tens to hundreds, and even approaching thousand qubits—together with error mitigation and hybrid quantum-classical architectures, have made the practical integration of quantum methods increasingly feasible. However, existing studies remain fragmented and task-specific, lacking a unified framework and an engineering-oriented assessment. This paper proposes a quantum-enhanced forecasting-scheduling pipeline that decomposes IES workflows into modular layers and systematically positions quantum methods within the closed loop. We show that, under Noisy Intermediate-Scale Quantum (NISQ) conditions, quantum approaches are most effective as plug-in computational enhancers, improving high-dimensional representation in forecasting and accelerating discrete optimization in scheduling. Based on this insight, we derive task-oriented method selection strategies and practical deployment guidelines, including a decision tree for layer selection, IES specific penalty tuning, and fallback mechanisms for constraint violation. The proposed framework clarifies applicability boundaries and provides a structured pathway for transitioning quantum-enhanced IES from theoretical studies to engineering practice.