Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

被引:63
|
作者
Heo, Yeonsook [1 ]
Augenbroe, Godfried [2 ]
Graziano, Diane [3 ]
Muehleisen, Ralph T. [3 ]
Guzowski, Leah [3 ]
机构
[1] Univ Cambridge, Dept Architecture, Cambridge, England
[2] Georgia Inst Technol, Coll Architecture, Atlanta, GA 30332 USA
[3] Argonne Natl Lab, Decis & Informat Sci Div, Lemont, IL USA
关键词
Large-scale retrofit analysis; Bayesian calibration; Normative model; Uncertainty analysis; SIMULATION; UNCERTAINTY;
D O I
10.1016/j.buildenv.2014.12.016
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustrates both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:342 / 350
页数:9
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