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Calibration and uncertainty analysis for computer models - A meta-model based approach for integrated building energy simulation

Calibration and uncertainty analysis for computer models - A meta-model based approach for integrated building energy simulation
Calibration and uncertainty analysis for computer models - A meta-model based approach for integrated building energy simulation

In energy and environment field models are constructed, in general, based on well-defined physical phenomena and properties. Calibration and uncertainty analysis hold a particular interest because models represent a simplification of reality and, therefore, it is necessary to quantify to what degree they are imperfect before employing them in design, prediction and decision making processes. Integrated building energy models attempt to describe the effect of various internal and external actions (weather, occupancy, appliances, etc.) through physical relations (both algebraic and differential) and they are being widely used to design and operate high performance buildings, which are an essential component of a global energy strategy to reduce carbon emission and fossil sources depletion. An approach oriented to systems and able to integrate effectively field measured data and computer simulations for calibration in the modeling process has the potential to revolutionize the way buildings are designed and operated, and to stimulate also the development of new technologies and solutions in the field. The research presented in this paper aims to represent an initial step towards this integrated approach.

Bayesian analysis, Gaussian processes, Kernel regression, Model calibration, Uncertainty and sensitivity analysis
0306-2619
627-641
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Aste, Niccolò
9f0175c5-0192-4167-ac2e-c3735c794fde
Moshksar, Reza
b4cbe035-aa06-47e4-b6be-07fc79162322
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Aste, Niccolò
9f0175c5-0192-4167-ac2e-c3735c794fde
Moshksar, Reza
b4cbe035-aa06-47e4-b6be-07fc79162322

Manfren, Massimiliano, Aste, Niccolò and Moshksar, Reza (2013) Calibration and uncertainty analysis for computer models - A meta-model based approach for integrated building energy simulation. Applied Energy, 103, 627-641. (doi:10.1016/j.apenergy.2012.10.031).

Record type: Article

Abstract

In energy and environment field models are constructed, in general, based on well-defined physical phenomena and properties. Calibration and uncertainty analysis hold a particular interest because models represent a simplification of reality and, therefore, it is necessary to quantify to what degree they are imperfect before employing them in design, prediction and decision making processes. Integrated building energy models attempt to describe the effect of various internal and external actions (weather, occupancy, appliances, etc.) through physical relations (both algebraic and differential) and they are being widely used to design and operate high performance buildings, which are an essential component of a global energy strategy to reduce carbon emission and fossil sources depletion. An approach oriented to systems and able to integrate effectively field measured data and computer simulations for calibration in the modeling process has the potential to revolutionize the way buildings are designed and operated, and to stimulate also the development of new technologies and solutions in the field. The research presented in this paper aims to represent an initial step towards this integrated approach.

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More information

Published date: 2013
Keywords: Bayesian analysis, Gaussian processes, Kernel regression, Model calibration, Uncertainty and sensitivity analysis

Identifiers

Local EPrints ID: 414101
URI: http://eprints.soton.ac.uk/id/eprint/414101
ISSN: 0306-2619
PURE UUID: 85e1d31b-f7cf-48ad-b489-d178239d0531
ORCID for Massimiliano Manfren: ORCID iD orcid.org/0000-0003-1438-970X

Catalogue record

Date deposited: 14 Sep 2017 16:31
Last modified: 06 Jun 2024 01:59

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Contributors

Author: Niccolò Aste
Author: Reza Moshksar

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