Grey-box Modelling of a Household Refrigeration Unit Using Time Series Data in Application to Demand Side Management
For researchers in demand side management, this work provides a practical modeling approach for household refrigeration units, though it is incremental as it applies existing methods to a specific appliance.
This paper applies stochastic grey-box modeling to identify power-to-temperature models of a domestic freezer, using SDEs and MLE. The models are validated and used in MPC for demand response, demonstrating the freezer's ability to shift electricity consumption.
This paper describes the application of stochastic grey-box modeling to identify electrical power consumption-to-temperature models of a domestic freezer using experimental measurements. The models are formulated using stochastic differential equations (SDEs), estimated by maximum likelihood estimation (MLE), validated through the model residuals analysis and cross-validated to detect model over-fitting. A nonlinear model based on the reversed Carnot cycle is also presented and included in the modeling performance analysis. As an application of the models, we apply model predictive control (MPC) to shift the electricity consumption of a freezer in demand response experiments, thereby addressing the model selection problem also from the application point of view and showing in an experimental context the ability of MPC to exploit the freezer as a demand side resource (DSR).