Shrinked-Space Search Method for LVCTs' Parameters Identification

Smart thermostats have become a promising device to control electric baseboard heaters' energy consumption while considering their flexibility in the demand response (DR) context. This article applies a shrinked-space search method to identify the tuning parameters of line voltage communicating thermostats (LVCTs). The proposed approach based on Bayesian optimization (BO) algorithm takes account of a reference model to drastically shrink the search space while enforcing the identification of a single set of parameters compatible with all arising dynamics of the controller and helping to establish an interpretable model. Furthermore, a subsequent integral tracking strategy has been adopted to convexify the objective function (for identification purposes) while considering the logical constraints governing the thermostat dynamics. This helps to recover the updating logic of the integral part of the controller model. The experimental validation results of eight LVCTs operating in an inhabited house show the effectiveness of the proposed method since it leads to establishing digital twins for the studied controllers. In addition, a case study is presented to demonstrate the usefulness of the reconstructed model in a DR framework.