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

TL;DR

This paper develops stochastic grey-box models of household freezers using time series data, validating them with experimental data, and demonstrates their use in demand response via model predictive control.

Contribution

It introduces stochastic differential equation models for freezer power-temperature dynamics and applies MPC for demand side management.

Findings

Models accurately predict freezer power consumption.

MPC effectively shifts electricity demand in experiments.

Grey-box models enhance demand response strategies.

Abstract

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).