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

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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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Funding Information
  • National Natural Science Foundation of China (52107102, 52321004)

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