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Modelling the heat dynamics of a building using stochastic differential equations

Andersen, Klaus Kaae, Madsen, Henrik, Hansen, Lars H.·2000·Energy and Buildings

Abstract

This paper describes the continuous time modelling of the heat dynamics of a building. The considered building is a residential like test house divided into two test rooms with a water based central heating. Each test room is divided into thermal zones in order to describe both short and long term variations. Besides modelling the heat transfer between thermal zones, attention is put on modelling the heat input from radiators and solar radiation. The applied modelling procedure is based on collected building performance data and statistical methods. The statistical methods are used in parameter estimation and model validation, while physical knowledge is used in forming the model structure. The suggested lumped parameter model is thus based on thermodynamics and formulated as a system of stochastic differential equations. Due to the continuous time formulation the parameters of the model are directly physical interpretable. Finally, the prediction and simulation performance of the model is illustrated.

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Metadata

Authors
Andersen, K. K., Madsen, H., & Hansen, L. H.
Venue
Energy and Buildings
Publisher
Elsevier BV
Published
2000-01
Volume
31
Issue
1
Pages
13-24
ISSN
0378-7788 (ISSN-L)
OpenAlex
W2089431628
Cited by
180
References
0
FWCI
8.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Modelling the heat dynamics of a building using stochastic differential equations. https://app.cochat.ai/papers/10.1016/s0378-7788(98)00069-3
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Andersen, K. K., Madsen, H., & Hansen, L. H. (2000). Modelling the heat dynamics of a building using stochastic differential equations. Energy and Buildings. https://doi.org/10.1016/s0378-7788(98)00069-3

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.