Grey-box modelling of a household refrigeration unit using time series data in application to demand side management

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).

Demand side management in refrigeration applications

“…They demonstrate that their strategies (acting on switching ON/OFF periods or temperature bounds) can also improve losses issue and voltage profile of power distribution systems. Paper [23] develops a gray-box model for a refrigerator, identified by means of experimental data, to be used in model predictive controls aimed at shifting the refrigerator load for DR purposes. This kind of modelling approach is also useful for Home Energy Management System (HEMS), as proposed in the work [24] that develops a system for executing DR autonomously.…”

Stochastic control of cooling appliances under disturbances for primary frequency reserves

“…Other, more involved models include multiple state variables and heat transfers to better understand the temperature evolution depending on the stored food and of the food itself [27]. As the focus of this paper is on the interaction of large populations of refrigerators with the power system, we decided to use the first order model.…”

Using Box Modeling to Determine Photodegradation Coefficients Describing the Removal of Gaseous Formaldehyde from Indoor Air

“…Box modeling techniques are applied widely on large scale environmental issues and engineering fields (Liu et al, 2000;Afram and Janabi-Sharifi, 2014;Sossan et al, 2016). Indoor air quality can be affected by VOCs, tinny particulates, microbes, or various types of hazardous pollutants which present a health risk to people.…”

“…Three processes of HCHO removal could be distinguished and evaluated as a natural dissipation, a photodegradation, and photocatalytic oxidation decomposition within our experimental validation. This study presents results that describe the heterogeneous catalytic reaction of these species in detail and helps engineers to design air handling units to achieve improved control strategies on the indoor air quality and the energy consumption efficiency (Afram and Janabi-Sharifi, 2014;Sossan et al, 2016).…”