Abstract
Retrofitting existing buildings is urgent given the increasing need to improve the energy efficiency of the existing building stock. This paper presents a scalable, probabilistic methodology that can support large scale investments in energy retrofit of buildings while accounting for uncertainty. The methodology is based on Bayesian calibration of normative energy models. Based on CEN-ISO standards, normative energy models are light-weight, quasi-steady state formulations of heat balance equations, which makes them appropriate for modeling large sets of buildings efficiently. Calibration of these models enables improved representation of the actual buildings and quantification of uncertainties associated with model parameters. In addition, the calibrated models can incorporate additional uncertainties coming from retrofit interventions to generate probabilistic predictions of retrofit performance. Probabilistic outputs can be straightforwardly translated to quantify risks of under-performance associated with retrofit interventions. A case study demonstrates that the proposed methodology with the use of normative models can correctly evaluate energy retrofit options and support risk conscious decision-making by explicitly inspecting risks associated with each retrofit option.
| Original language | English |
|---|---|
| Pages (from-to) | 550-560 |
| Number of pages | 11 |
| Journal | Energy and Buildings |
| Volume | 47 |
| DOIs | |
| Publication status | Published - 2012 Apr 1 |
| Externally published | Yes |
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Keywords
- Bayesian calibration
- Normative energy models
- Retrofit analysis
- Uncertainty analysis
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Mechanical Engineering
- Electrical and Electronic Engineering
Cite this
Calibration of building energy models for retrofit analysis under uncertainty. / Heo, Yeonsook; Choudhary, R.; Augenbroe, G. A.
In: Energy and Buildings, Vol. 47, 01.04.2012, p. 550-560.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Calibration of building energy models for retrofit analysis under uncertainty
AU - Heo, Yeonsook
AU - Choudhary, R.
AU - Augenbroe, G. A.
PY - 2012/4/1
Y1 - 2012/4/1
N2 - Retrofitting existing buildings is urgent given the increasing need to improve the energy efficiency of the existing building stock. This paper presents a scalable, probabilistic methodology that can support large scale investments in energy retrofit of buildings while accounting for uncertainty. The methodology is based on Bayesian calibration of normative energy models. Based on CEN-ISO standards, normative energy models are light-weight, quasi-steady state formulations of heat balance equations, which makes them appropriate for modeling large sets of buildings efficiently. Calibration of these models enables improved representation of the actual buildings and quantification of uncertainties associated with model parameters. In addition, the calibrated models can incorporate additional uncertainties coming from retrofit interventions to generate probabilistic predictions of retrofit performance. Probabilistic outputs can be straightforwardly translated to quantify risks of under-performance associated with retrofit interventions. A case study demonstrates that the proposed methodology with the use of normative models can correctly evaluate energy retrofit options and support risk conscious decision-making by explicitly inspecting risks associated with each retrofit option.
AB - Retrofitting existing buildings is urgent given the increasing need to improve the energy efficiency of the existing building stock. This paper presents a scalable, probabilistic methodology that can support large scale investments in energy retrofit of buildings while accounting for uncertainty. The methodology is based on Bayesian calibration of normative energy models. Based on CEN-ISO standards, normative energy models are light-weight, quasi-steady state formulations of heat balance equations, which makes them appropriate for modeling large sets of buildings efficiently. Calibration of these models enables improved representation of the actual buildings and quantification of uncertainties associated with model parameters. In addition, the calibrated models can incorporate additional uncertainties coming from retrofit interventions to generate probabilistic predictions of retrofit performance. Probabilistic outputs can be straightforwardly translated to quantify risks of under-performance associated with retrofit interventions. A case study demonstrates that the proposed methodology with the use of normative models can correctly evaluate energy retrofit options and support risk conscious decision-making by explicitly inspecting risks associated with each retrofit option.
KW - Bayesian calibration
KW - Normative energy models
KW - Retrofit analysis
KW - Uncertainty analysis
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U2 - 10.1016/j.enbuild.2011.12.029
DO - 10.1016/j.enbuild.2011.12.029
M3 - Article
AN - SCOPUS:84857063485
VL - 47
SP - 550
EP - 560
JO - Energy and Buildings
JF - Energy and Buildings
SN - 0378-7788
ER -