Non-linear grey-box modelling for heat dynamics of buildings

This paper introduces a non-linear grey-box (GB) model based on stochastic differential equations that describes the heat dynamics of a school building in Denmark, equipped with a water-based heating sys-tem. The building is connected to a local district heating network through a heat exchanger. The heat is delivered to the rooms mainly through radiators and partially through a ventilation system. A monitoring system based on IoT sensors provides data on indoor climate in the rooms and on the heat load of the building. Using this data, we estimate unknown states and parameters of a model of the building's heat -ing system using the maximum likelihood method. Important novelties of this paper include models of the water flow in the circuit and the state of the valves in the radiator thermostats. The non-linear model accurately predicts the indoor air temperature, return water temperature and heat load. The ideas behind the model lay a foundation for GB models of buildings that use different kinds of water-based heating systems such as air-to-water/water-to-water heat pumps. Such GB models enable model predictive con-trol to control e.g. the indoor air climate or provide flexibility services. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This paper presents a non-linear grey-box model based on stochastic differential equations to describe the heat dynamics of a school building in Denmark equipped with a water-based heating system. The model accurately predicts indoor air temperature, return water temperature, and heat load, laying the foundation for grey-box models of buildings using different types of water-based heating systems.