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dc.contributor.authorHeo, Yeonsooken_US
dc.date.accessioned2012-02-17T19:25:23Z
dc.date.available2012-02-17T19:25:23Z
dc.date.issued2011-11-10en_US
dc.identifier.urihttp://hdl.handle.net/1853/42878
dc.description.abstractRetrofitting of existing buildings is essential to reach reduction targets in energy consumption and greenhouse gas emission. In the current practice of a retrofit decision process, professionals perform energy audits, and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions. In order to enhance the reliability of simulation models, they typically calibrate simulation models based on monitored energy use data. The calibration techniques used for this purpose are manual and expert-driven. The current practice has major drawbacks: (1) the modeling and calibration methods do not scale to large portfolio of buildings due to their high costs and heavy reliance on expertise, and (2) the resulting deterministic models do not provide insight into underperforming risks associated with each retrofit intervention. This thesis has developed a new retrofit analysis framework that is suitable for large-scale analysis and risk-conscious decision-making. The framework is based on the use of normative models and Bayesian calibration techniques. Normative models are light-weight quasi-steady state energy models that can scale up to large sets of buildings, i.e. to city and regional scale. In addition, they do not require modeling expertise since they follow a set of modeling rules that produce a standard measure for energy performance. The normative models are calibrated under a Bayesian approach such that the resulting calibrated models quantify uncertainties in the energy outcomes of a building. Bayesian calibration models can also incorporate additional uncertainties associated with retrofit interventions to generate probability distributions of retrofit performance. Probabilistic outputs can be straightforwardly translated into a measure that quantifies underperforming risks of retrofit interventions and thus enable decision making relative to the decision-makers' rational objectives and risk attitude. This thesis demonstrates the feasibility of the new framework on retrofit applications by verifying the following two hypotheses: (1) normative models supported by Bayesian calibration have sufficient model fidelity to adequately support retrofit decisions, and (2) they can support risk-conscious decision-making by explicitly quantifying risks associated with retrofit options. The first and second hypotheses are examined through case studies that compare outcomes from the calibrated normative model with those from a similarly calibrated transient simulation model and compare decisions derived by the proposed framework with those derived by standard practices respectively. The new framework will enable cost-effective retrofit analysis at urban scale with explicit management of uncertainties.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectUncertainty analysisen_US
dc.subjectBayesian calibrationen_US
dc.subjectNormative energy modelsen_US
dc.subjectRetrofit analysisen_US
dc.subject.lcshDecision making Testing
dc.subject.lcshArchitecture Decision making
dc.subject.lcshUncertainty
dc.titleBayesian calibration of building energy models for energy retrofit decision-making under uncertaintyen_US
dc.typeDissertationen_US
dc.description.degreePhDen_US
dc.contributor.departmentArchitectureen_US
dc.description.advisorCommittee Chair: Augenbroe, Godfried; Committee Member: Choudhary, Ruchi; Committee Member: Guillas, Serge; Committee Member: Park, Cheol-Soo; Committee Member: Wu, Jeffen_US


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