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This paper describes the application of stochastic grey-box modeling toidentify electrical power consumption-to-temperature models of a domesticfreezer using experimental measurements. The models are formulated usingstochastic differential equations (SDEs), estimated by maximum likelihoodestimation (MLE), validated through the model residuals analysis andcross-validated to detect model over-fitting. A nonlinear model based on thereversed Carnot cycle is also presented and included in the modelingperformance analysis. As an application of the models, we apply modelpredictive control (MPC) to shift the electricity consumption of a freezer indemand response experiments, thereby addressing the model selection problemalso from the application point of view and showing in an experimental contextthe ability of MPC to exploit the freezer as a demand side resource (DSR).

Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management