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Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach

Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach
Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach

The increased awareness on sustainability matters is contributing to the evolution of energy and environmental policies for the building sector at the EU level, oriented toward resource efficiency. There exist today several possible strategies to model building performance through the life cycle. The increase of available computational capacity and of data acquisition capability is opening new scenarios for practical applications, which can contribute to the reduction of the gap usually encountered between simulated and measured energy performance. This article aims to investigate an approach for probabilistic building performance simulation to be used across life cycle phases, employing reduced-order models for performance monitoring and energy management. The workflow proposed aims to establish a continuity among design and operation phases. Design phase simulation is generally subject to relevant temporal and economic constraints and a successful workflow should incorporate elements from current design practices but should also add new features, which have to be reasonably automated to reduce additional effort. Therefore, the workflow proposed is automated and tested for robustness using Monte Carlo technique. In the design phase, the approach can be used for identifying probabilistic performance bounds suitable for risk analysis in energy efficiency investments, employing cost-optimal or life cycle cost accounting methodologies. In the operation phase, it can be used for performance monitoring and energy management based on daily energy consumption analysis, similarly to other multivariate regression-based methods at the state of the art, addressing the problem of maintaining energy consumption and related costs constantly under control.

Behavioral learning, Behavioral modeling, Building performance simulation, Energy efficiency, Energy management, Probabilistic modeling, Uncertainty propagation
0378-7788
128-141
Cecconi, Fulvio Re
76eb2fda-8399-4b29-ae1d-2d537cbf5327
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Tagliabue, Lavinia Chiara
30e84a7d-5ac8-47fc-9a45-10233778402a
Ciribini, Angelo Luigi Camillo
c895dc29-b5a2-4db7-ab68-04a3a6c79704
De Angelis, Enrico
ea55c031-024d-4b1e-a1cb-5a6d97bb1d6a
Cecconi, Fulvio Re
76eb2fda-8399-4b29-ae1d-2d537cbf5327
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Tagliabue, Lavinia Chiara
30e84a7d-5ac8-47fc-9a45-10233778402a
Ciribini, Angelo Luigi Camillo
c895dc29-b5a2-4db7-ab68-04a3a6c79704
De Angelis, Enrico
ea55c031-024d-4b1e-a1cb-5a6d97bb1d6a

Cecconi, Fulvio Re, Manfren, Massimiliano, Tagliabue, Lavinia Chiara, Ciribini, Angelo Luigi Camillo and De Angelis, Enrico (2017) Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach. Energy and Buildings, 148, 128-141. (doi:10.1016/j.enbuild.2017.05.013).

Record type: Article

Abstract

The increased awareness on sustainability matters is contributing to the evolution of energy and environmental policies for the building sector at the EU level, oriented toward resource efficiency. There exist today several possible strategies to model building performance through the life cycle. The increase of available computational capacity and of data acquisition capability is opening new scenarios for practical applications, which can contribute to the reduction of the gap usually encountered between simulated and measured energy performance. This article aims to investigate an approach for probabilistic building performance simulation to be used across life cycle phases, employing reduced-order models for performance monitoring and energy management. The workflow proposed aims to establish a continuity among design and operation phases. Design phase simulation is generally subject to relevant temporal and economic constraints and a successful workflow should incorporate elements from current design practices but should also add new features, which have to be reasonably automated to reduce additional effort. Therefore, the workflow proposed is automated and tested for robustness using Monte Carlo technique. In the design phase, the approach can be used for identifying probabilistic performance bounds suitable for risk analysis in energy efficiency investments, employing cost-optimal or life cycle cost accounting methodologies. In the operation phase, it can be used for performance monitoring and energy management based on daily energy consumption analysis, similarly to other multivariate regression-based methods at the state of the art, addressing the problem of maintaining energy consumption and related costs constantly under control.

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More information

Accepted/In Press date: 5 May 2017
e-pub ahead of print date: 8 May 2017
Published date: 1 August 2017
Keywords: Behavioral learning, Behavioral modeling, Building performance simulation, Energy efficiency, Energy management, Probabilistic modeling, Uncertainty propagation

Identifiers

Local EPrints ID: 421345
URI: https://eprints.soton.ac.uk/id/eprint/421345
ISSN: 0378-7788
PURE UUID: 57ad6576-8f56-4e56-a73f-9f1c25e39a7f

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Date deposited: 01 Jun 2018 16:31
Last modified: 01 Aug 2018 16:31

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Contributors

Author: Fulvio Re Cecconi
Author: Lavinia Chiara Tagliabue
Author: Angelo Luigi Camillo Ciribini
Author: Enrico De Angelis

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