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Modelling the thermal dynamics of buildings: a latent force model based approach

Modelling the thermal dynamics of buildings: a latent force model based approach
Modelling the thermal dynamics of buildings: a latent force model based approach
Minimizing the energy consumed on heating, ventilation and air conditioning (HVAC) systems of residential buildings, without impacting occupants’ comfort has been highlighted as an important artificial intelligence (AI) challenge. Typically, approaches that seek to address this challenge use a model that captures the thermal dynamics within a building, also referred to as a thermal model. In this paper, we introduce a novel thermal model, which we refer to as a latent force thermal model of the thermal dynamics of a building or LFM-TM. Our model is derived from an existing grey-box thermal model, which is augmented with an extra term referred to as the learned residual. This term is capable of modelling the effect of any a priori unknown additional dynamic, which if not captured, appears as structure in a thermal models residual (the error induced by the model). More importantly, the learned residual can also capture the effects of physical elements such as a building’s envelope or the lags in a heating system, leading to a significant reduction in complexity compared to existing models. We evaluate the performance of LFM-TM on two independent data sources: the FlexHouse data, which was previously used for evaluating the efficacy of existing grey-box models [Bacher and Madsen 2011], and heating data logged within homes located on University of Southampton campus. On both datasets, we show that our approach outperforms existing models in its ability to accurately fit the observed data, generate accurate day-ahead internal temperature predictions and explain a large amount of the variability in the future observations.
2157-6904
7:1-7:27
Ghosh, Siddhartha
abaf1e1d-3b5f-4a61-913e-e61273ed3790
Reece, Steve
9eecf0b5-3207-4067-9b72-6abcea5b54ab
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Roberts, Stephen
fef5d01c-92bd-44cf-93f0-923ec24f8875
Malibari, Areej
ac1d9fde-3ad4-4373-9a3a-2f81eb533c14
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Ghosh, Siddhartha
abaf1e1d-3b5f-4a61-913e-e61273ed3790
Reece, Steve
9eecf0b5-3207-4067-9b72-6abcea5b54ab
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Roberts, Stephen
fef5d01c-92bd-44cf-93f0-923ec24f8875
Malibari, Areej
ac1d9fde-3ad4-4373-9a3a-2f81eb533c14
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Ghosh, Siddhartha, Reece, Steve, Rogers, Alex, Roberts, Stephen, Malibari, Areej and Jennings, Nicholas R. (2015) Modelling the thermal dynamics of buildings: a latent force model based approach. ACM Transactions on Intelligent Systems and Technology, 6 (1), 7:1-7:27. (doi:10.1145/2629674).

Record type: Article

Abstract

Minimizing the energy consumed on heating, ventilation and air conditioning (HVAC) systems of residential buildings, without impacting occupants’ comfort has been highlighted as an important artificial intelligence (AI) challenge. Typically, approaches that seek to address this challenge use a model that captures the thermal dynamics within a building, also referred to as a thermal model. In this paper, we introduce a novel thermal model, which we refer to as a latent force thermal model of the thermal dynamics of a building or LFM-TM. Our model is derived from an existing grey-box thermal model, which is augmented with an extra term referred to as the learned residual. This term is capable of modelling the effect of any a priori unknown additional dynamic, which if not captured, appears as structure in a thermal models residual (the error induced by the model). More importantly, the learned residual can also capture the effects of physical elements such as a building’s envelope or the lags in a heating system, leading to a significant reduction in complexity compared to existing models. We evaluate the performance of LFM-TM on two independent data sources: the FlexHouse data, which was previously used for evaluating the efficacy of existing grey-box models [Bacher and Madsen 2011], and heating data logged within homes located on University of Southampton campus. On both datasets, we show that our approach outperforms existing models in its ability to accurately fit the observed data, generate accurate day-ahead internal temperature predictions and explain a large amount of the variability in the future observations.

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More information

e-pub ahead of print date: 2014
Published date: April 2015
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 362898
URI: https://eprints.soton.ac.uk/id/eprint/362898
ISSN: 2157-6904
PURE UUID: e5f481aa-87ea-42b9-94d6-6502c1695fa6

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Date deposited: 07 Mar 2014 22:21
Last modified: 16 Jul 2019 21:10

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