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arXiv · 2608.24390

Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse Optimization

Ruike Lyu·Hongye Guo·Goran Strbac·Chongqing Kang
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Abstract

The intricate mixed-integer constraints in industrial load models not only pose challenges for their direct integration into economic dispatch or market clearing processes but also render current analytical dimension-reduction methods ineffective. We propose a novel data-driven dimension-reduction approach for industrial load modeling, which uses the optimal energy usage data from industrial loads to train a dimension-reduced model that best fits the original constraints. Our approach, implemented by the adjustable load fleet model, outperformed analytical methods across three industrial load datasets.

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Ruike Lyu, Hongye Guo, Goran Strbac, Chongqing Kang. 2026-08-25. Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse Optimization. https://doi.org/10.1109/tsg.2025.3545339

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