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

Christian Ankerstjerne Thilker; Peder Bacher; Hjörleifur G. Bergsteinsson; Rune Grønborg Junker; Davide Cali; Henrik Madsen

doi:10.1016/j.enbuild.2021.111457

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

Christian Ankerstjerne Thilker^*
, Peder Bacher
, Hjörleifur G. Bergsteinsson
, Rune Grønborg Junker
, Davide Cali
, Henrik Madsen

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

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 system. 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 heating 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 control to control e.g. the indoor air climate or provide flexibility services.

Original language	English
Article number	111457
Journal	Energy and Buildings
Volume	252
Number of pages	10
ISSN	0378-7788
DOIs	https://doi.org/10.1016/j.enbuild.2021.111457
Publication status	Published - 1 Dec 2021

Bibliographical note

Funding Information:
The authors received funding from the following projects; Sustainable plus energy neighbourhoods (syn.ikia) (H2020 No. 869918), Centre for IT-Intelligent Energy Systems (CITIES) (DSF 1305-00027B), Top-Up (Innovation Fund Denmark 9045-00017B), SCA+ (Interreg Öresund-Kattegat-Skagerrak), Research Centre on Zero Emission Neighbourhoods in Smart Cities (FME-ZEN) (Research Council of Norway, No. 257660), and Flexibile Energy Denmark (FED) (IFD 8090-00069B).

Publisher Copyright:
© 2021 The Author(s)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

District heating
Grey-box models
Non-linear models
Smart energy systems
Stochastic differential equations

Access to Document

10.1016/j.enbuild.2021.111457

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Cite this

@article{dcd52f6128354a6e9abe1c9e0d7d9fde,

title = "Non-linear grey-box modelling for heat dynamics of buildings",

abstract = "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 system. 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 heating 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 control to control e.g. the indoor air climate or provide flexibility services.",

keywords = "District heating, Grey-box models, Non-linear models, Smart energy systems, Stochastic differential equations",

author = "Thilker, \{Christian Ankerstjerne\} and Peder Bacher and Bergsteinsson, \{Hj{\"o}rleifur G.\} and Junker, \{Rune Gr{\o}nborg\} and Davide Cali and Henrik Madsen",

note = "Funding Information: The authors received funding from the following projects; Sustainable plus energy neighbourhoods (syn.ikia) (H2020 No. 869918), Centre for IT-Intelligent Energy Systems (CITIES) (DSF 1305-00027B), Top-Up (Innovation Fund Denmark 9045-00017B), SCA+ (Interreg {\"O}resund-Kattegat-Skagerrak), Research Centre on Zero Emission Neighbourhoods in Smart Cities (FME-ZEN) (Research Council of Norway, No. 257660), and Flexibile Energy Denmark (FED) (IFD 8090-00069B). Publisher Copyright: {\textcopyright} 2021 The Author(s)",

year = "2021",

month = dec,

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doi = "10.1016/j.enbuild.2021.111457",

language = "English",

volume = "252",

journal = "Energy and Buildings",

issn = "0378-7788",

publisher = "Elsevier",

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T1 - Non-linear grey-box modelling for heat dynamics of buildings

AU - Thilker, Christian Ankerstjerne

AU - Bacher, Peder

AU - Bergsteinsson, Hjörleifur G.

AU - Junker, Rune Grønborg

AU - Cali, Davide

AU - Madsen, Henrik

N1 - Funding Information: The authors received funding from the following projects; Sustainable plus energy neighbourhoods (syn.ikia) (H2020 No. 869918), Centre for IT-Intelligent Energy Systems (CITIES) (DSF 1305-00027B), Top-Up (Innovation Fund Denmark 9045-00017B), SCA+ (Interreg Öresund-Kattegat-Skagerrak), Research Centre on Zero Emission Neighbourhoods in Smart Cities (FME-ZEN) (Research Council of Norway, No. 257660), and Flexibile Energy Denmark (FED) (IFD 8090-00069B). Publisher Copyright: © 2021 The Author(s)

PY - 2021/12/1

Y1 - 2021/12/1

N2 - 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 system. 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 heating 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 control to control e.g. the indoor air climate or provide flexibility services.

AB - 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 system. 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 heating 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 control to control e.g. the indoor air climate or provide flexibility services.

KW - District heating

KW - Grey-box models

KW - Non-linear models

KW - Smart energy systems

KW - Stochastic differential equations

U2 - 10.1016/j.enbuild.2021.111457

DO - 10.1016/j.enbuild.2021.111457

M3 - Journal article

AN - SCOPUS:85115737684

SN - 0378-7788

VL - 252

JO - Energy and Buildings

JF - Energy and Buildings

M1 - 111457

ER -

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

Abstract

Bibliographical note

UN SDGs

Keywords

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