Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse Optimization
arXiv:2608.24390 · doi:10.1109/TSG.2025.3545339
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.
Manuscript accepted by IEEE Power Engineering Letters (Published in: IEEE Transactions on Smart Grid ( Volume: 16, Issue: 3, May 2025))