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
26-40
Tronchin, Lamberto
8527a327-51fb-4865-b99d-eab721dadec9
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
James, Patrick AB
da0be14a-aa63-46a7-8646-a37f9a02a71b
15 December 2018
Tronchin, Lamberto
8527a327-51fb-4865-b99d-eab721dadec9
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), .
(doi:10.1016/j.energy.2018.09.037).
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).
Text
2018_08_31_Manfren_Manuscript
- Accepted Manuscript
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
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Date deposited: 29 Oct 2018 17:30
Last modified: 18 Mar 2024 05:20
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Author:
Lamberto Tronchin
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