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

Ruike Lyu, Hongye Guo, Goran Strbac, Chongqing Kang

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.