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Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building

Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building
Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building

Efficient buildings are an essential component of sustainability and energy transitions, which represent today a techno-economic and socio-economic problem. New paradigms are emerging both for new and existing buildings (e.g. NZEBs) and passive design strategies are becoming increasingly common. However, the adoption of these strategies in mild climates has to be carefully evaluated to prevent overheating in intermediate seasons and increasing cooling loads in summer, considering also climate change scenarios. Additionally, optimistic assumptions about building technology performance are often considered and the variability of occupant comfort preferences and behaviour is generally neglected in the design phase. The research presented aims at verifying the suitability of a simple, robust and scalable calibration approach (based on multivariate linear regression) to link design and operational performance analysis transparently, using a Passive House case study building. First, the original baseline design configuration is compared with a larger spectrum of data generated by means of parametric simulation, following a Design of Experiment (DOE) approach. After that, regression models are trained first on simulation data and then progressively calibrated on measured data during a three year monitoring period. The two fundamental objectives are evaluating the robustness of design phase performance analysis through parametric simulation (i.e. detecting potentially critical assumptions) and maintaining a continuity with operation phase performance analysis (i.e. exploiting the feed-back from measured data).

Behavioural modelling, Building performance simulation, Multivariate regression, Parametric modelling, Passive House, Performance monitoring
0360-5442
26-40
Tronchin, Lamberto
550312d9-21be-41d8-8947-a0d6fb5c52a8
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
James, Patrick AB
da0be14a-aa63-46a7-8646-a37f9a02a71b
Tronchin, Lamberto
550312d9-21be-41d8-8947-a0d6fb5c52a8
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
James, Patrick AB
da0be14a-aa63-46a7-8646-a37f9a02a71b

Tronchin, Lamberto, Manfren, Massimiliano and James, Patrick AB (2018) Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building. Energy, 165 (Part A), 26-40. (doi:10.1016/j.energy.2018.09.037).

Record type: Article

Abstract

Efficient buildings are an essential component of sustainability and energy transitions, which represent today a techno-economic and socio-economic problem. New paradigms are emerging both for new and existing buildings (e.g. NZEBs) and passive design strategies are becoming increasingly common. However, the adoption of these strategies in mild climates has to be carefully evaluated to prevent overheating in intermediate seasons and increasing cooling loads in summer, considering also climate change scenarios. Additionally, optimistic assumptions about building technology performance are often considered and the variability of occupant comfort preferences and behaviour is generally neglected in the design phase. The research presented aims at verifying the suitability of a simple, robust and scalable calibration approach (based on multivariate linear regression) to link design and operational performance analysis transparently, using a Passive House case study building. First, the original baseline design configuration is compared with a larger spectrum of data generated by means of parametric simulation, following a Design of Experiment (DOE) approach. After that, regression models are trained first on simulation data and then progressively calibrated on measured data during a three year monitoring period. The two fundamental objectives are evaluating the robustness of design phase performance analysis through parametric simulation (i.e. detecting potentially critical assumptions) and maintaining a continuity with operation phase performance analysis (i.e. exploiting the feed-back from measured data).

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2018_08_31_Manfren_Manuscript - Accepted Manuscript
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More information

Accepted/In Press date: 5 September 2018
e-pub ahead of print date: 8 September 2018
Published date: 15 December 2018
Keywords: Behavioural modelling, Building performance simulation, Multivariate regression, Parametric modelling, Passive House, Performance monitoring

Identifiers

Local EPrints ID: 425634
URI: http://eprints.soton.ac.uk/id/eprint/425634
ISSN: 0360-5442
PURE UUID: 84e9fed7-1f6c-48ef-8c2d-f963c269b03d
ORCID for Massimiliano Manfren: ORCID iD orcid.org/0000-0003-1438-970X
ORCID for Patrick AB James: ORCID iD orcid.org/0000-0002-2694-7054

Catalogue record

Date deposited: 29 Oct 2018 17:30
Last modified: 07 Oct 2020 06:50

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