发表机构
University Politehnica Timisoara（蒂米什瓦拉理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出三相数据驱动孪生框架，结合Hankel-Koopman能量分解与逆校准NARX模型，实现耦合实验数据的有限时域预测，并在太阳能案例中验证高精度。
AI 中文摘要
本文提出了一种统一的三相数据驱动孪生框架，用于从耦合实验测量中对物理量进行有限时域预测。该框架结合了新的Hankel-Koopman有限时域能量分解与正交模态，以及一个经过逆校准的多输出非线性自回归模型，该模型带有外生输入。非线性动力学在规定的校准窗口内于Hankel系数空间中被识别和模拟。随后，在物理测量空间中，通过反对角恢复和相应轨迹的重构来评估候选模型。所得的模拟通道轨迹作为外生输入，输入到针对目标量的递归预测模型中。通过这种方式，所提出的框架集成了降阶表示、非线性动力学识别、测量重构和可解释预测。数学分析将Koopman延迟坐标理论专门应用于序列化多通道Hankel提升，并确立了其与移位Hankel表示的兼容性。它还证明了模态的正交性和有限时域模态能量分解，以及在明确陈述的假设下所识别模型的存在性、唯一性、逆稳定性和前向稳定性。在一个太阳能发电厂案例研究中，该框架在所有考虑的预测时域内均产生了一致的高相关性和低相对误差，展示了其在递归预测过程中保持目标量时间演化的能力。
英文摘要
This paper introduces a unified three-phase data-driven twin framework for finite-horizon forecasting of physical quantities from coupled experimental measurements. The framework combines a new Hankel--Koopman finite-horizon energy decomposition with orthogonal modes and an inverse-calibrated multi-output nonlinear autoregressive model with exogenous inputs. The nonlinear dynamics are identified and simulated in the Hankel coefficient space over a prescribed calibration window. Candidate models are then evaluated after anti-diagonal recovery and reconstruction of the corresponding trajectories in the physical measurement space. The resulting simulated channel trajectories serve as exogenous inputs to a recursive forecasting model for the quantity of interest. In this way, the proposed framework integrates reduced-order representation, nonlinear dynamical identification, measurement reconstruction, and explainable forecasting. The mathematical analysis specializes Koopman delay-coordinate theory to the serialized multichannel Hankel lift and establishes its compatibility with the shifted Hankel representation. It also proves the orthogonality of the modes and the finite-horizon modal energy decomposition, together with the existence, uniqueness, inverse stability, and forward stability of the identified models under explicitly stated hypotheses. In a solar-power-plant case study, the framework yields consistently high correlations and low relative errors across all considered forecast horizons, demonstrating its ability to preserve the temporal evolution of the quantity of interest during recursive forecasting.
