After establishing the mathematical models, VPPs often face the challenge of estimating specific model parameters, as they typically lack direct access to the complete parameters of individual DERs. This chapter takes industrial users as a case study to demonstrate how to estimate the parameters of polytope models introduced in Chap. 2. For industrial production users, general-purpose models such as the state-task network (STN) are commonly employed to capture the energy consumption constraints of industrial processes. However, the absence of directly accessible data–due to its private nature–prevents the precise parameterization of these models, hindering the accurate representation of industrial loads. To address this issue, this chapter introduces a novel approach, termed Production Scheduling Identification (PSI), which uses an inverse-optimization-based framework for industrial load modeling under incomplete information. PSI utilizes smart meter data from industrial users to infer production scheduling parameters, thereby enabling accurate load modeling without direct access to private data. The PSI approach is implemented with a modified STN model, and a practical algorithm is proposed to efficiently obtain effective solutions.