Thermal Models for Intelligent Heating of Buildings

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Standard

Thermal Models for Intelligent Heating of Buildings. / Thavlov, Anders; Bindner, Henrik W.

Proceedings of the International Conference on Applied Energy, ICAE 2012. 2012. p. A10591.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Harvard

Thavlov, A & Bindner, HW 2012, 'Thermal Models for Intelligent Heating of Buildings'. in Proceedings of the International Conference on Applied Energy, ICAE 2012. pp. A10591.

APA

Thavlov, A., & Bindner, H. W. (2012). Thermal Models for Intelligent Heating of Buildings. In Proceedings of the International Conference on Applied Energy, ICAE 2012. (pp. A10591)

CBE

Thavlov A, Bindner HW. 2012. Thermal Models for Intelligent Heating of Buildings. In Proceedings of the International Conference on Applied Energy, ICAE 2012. pp. A10591.

MLA

Thavlov, Anders and Henrik W. Bindner "Thermal Models for Intelligent Heating of Buildings". Proceedings of the International Conference on Applied Energy, ICAE 2012. 2012. A10591.

Vancouver

Thavlov A, Bindner HW. Thermal Models for Intelligent Heating of Buildings. In Proceedings of the International Conference on Applied Energy, ICAE 2012. 2012. p. A10591.

Author

Thavlov, Anders; Bindner, Henrik W. / Thermal Models for Intelligent Heating of Buildings.

Proceedings of the International Conference on Applied Energy, ICAE 2012. 2012. p. A10591.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Bibtex

@inbook{90a99af80f5e4f32941fc6edca804e42,
title = "Thermal Models for Intelligent Heating of Buildings",
keywords = "Heat dynamic modelling, Demand side management, Flexible load, Smart grid, Power system services",
author = "Anders Thavlov and Bindner, {Henrik W.}",
year = "2012",
pages = "A10591",
booktitle = "Proceedings of the International Conference on Applied Energy, ICAE 2012",

}

RIS

TY - GEN

T1 - Thermal Models for Intelligent Heating of Buildings

A1 - Thavlov,Anders

A1 - Bindner,Henrik W.

AU - Thavlov,Anders

AU - Bindner,Henrik W.

PY - 2012

Y1 - 2012

N2 - The Danish government has set the ambitious goal that the share of the total Danish electricity consumption, covered by wind energy, should be increased to 50% by year 2020. This asks for radical changes in how we utilize and transmit electricity in the future power grid. To fully utilize the high share of renewable power generation, which is in general intermittent and non-controllable, the<br/>consumption side has to be much more flexible than today. To achieve such flexibility, methods for moving power consumption in time, within the hourly timescale, have to be developed. One approach currently being pursued is to use the heat capacity of the thermal mass in buildings to temporarily store excess power production by increasing the electrical heating. Likewise can the electrical heating be postponed in periods with lack of production. To exploit the potential in thermal storage and to ensure the comfort of residents, proper prediction models for indoor temperature have to be developed. This paper presents a model for prediction of indoor temperature and power consumption from electrical space heating in an office building, using stochastic differential equations. The heat dynamic model is build using a grey box approach, i.e. by formulating the model using physical knowledge about heat flow, while the parameters in the model are estimated using collected data and statistics. The physical parameters in the model, e.g. heat capacities and resistances to transfer heat, have been estimated for an actual office building using a maximum likelihood technique.

AB - The Danish government has set the ambitious goal that the share of the total Danish electricity consumption, covered by wind energy, should be increased to 50% by year 2020. This asks for radical changes in how we utilize and transmit electricity in the future power grid. To fully utilize the high share of renewable power generation, which is in general intermittent and non-controllable, the<br/>consumption side has to be much more flexible than today. To achieve such flexibility, methods for moving power consumption in time, within the hourly timescale, have to be developed. One approach currently being pursued is to use the heat capacity of the thermal mass in buildings to temporarily store excess power production by increasing the electrical heating. Likewise can the electrical heating be postponed in periods with lack of production. To exploit the potential in thermal storage and to ensure the comfort of residents, proper prediction models for indoor temperature have to be developed. This paper presents a model for prediction of indoor temperature and power consumption from electrical space heating in an office building, using stochastic differential equations. The heat dynamic model is build using a grey box approach, i.e. by formulating the model using physical knowledge about heat flow, while the parameters in the model are estimated using collected data and statistics. The physical parameters in the model, e.g. heat capacities and resistances to transfer heat, have been estimated for an actual office building using a maximum likelihood technique.

KW - Heat dynamic modelling

KW - Demand side management

KW - Flexible load

KW - Smart grid

KW - Power system services

BT - Proceedings of the International Conference on Applied Energy, ICAE 2012

T2 - Proceedings of the International Conference on Applied Energy, ICAE 2012

SP - A10591

ER -