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search.filters.applied.f.advisor: Augenbroe, Godfried × End date: 2011 × Start date: 2011 × search.filters.applied.f.isSeriesOfPublicationTitle: Doctor of Philosophy with a Major in Architecture × search.filters.applied.f.resourcesubtype: Dissertation ×

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Now showing 1 - 3 of 3
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    Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty
    (Georgia Institute of Technology, 2011-11-10) Heo, Yeonsook
    Retrofitting 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.
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    Development of robust building energy demand-side control strategy under uncertainty
    (Georgia Institute of Technology, 2011-05-25) Kim, Sean Hay
    The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.
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    Theoretical framework for determinants of A/E/C firm value, strategy and continuity: an analysis incorporating corporeal, volitional and knowledge assets
    (Georgia Institute of Technology, 2011-03-11) Beard, Jeffrey L.
    This research project endeavors to frame a methodology that can be used to categorize firm value strategies (production logics) and choices of factor inputs (tangible and intangible assets), which are used to fuel production cycles for goods and services outputs. A secondary goal of the research is to attempt to determine what asset group combinations (resources) are combined by various classes of firms to produce sustainable outcomes for the A/E/C firms in the survey. The National Bureau of Economic Research recently issued a system of national accounts (acknowledging both tangible and intangible assets) that reflects the macro-economy but at the same juncture, lamented the fact that a firm-level micro-economic schema did not exist to mirror the national system. This study makes an effort to redress that void by investigating how such a system of accounts - measured on the input side of the ledger -- could begin to fill in a gap in information and understanding as pointed out by participants in the National Academy of Sciences symposium of 2009 entitled "Intangible Assets: Measuring and Enhancing Their Contribution to Corporate Value and Growth." In brief, the research represents an effort to make a contribution to a growing body of knowledge about intangible assets by solidifying a framework within which both tangible and intangible assets may be more appropriately conceptualized and more adequately measured for purposes of current and future investigations. The research also provides a methodology for beginning to understand how some design and construction industry firms rely on specific asset categories for operating success, corporate stock value and business continuity. It is conceivable that managers would use a variation of the methodology to better balance ongoing investments in their firm's portfolio of tangible and intangible resources. The mixed methods used in this research support the following conclusions: 1) In terms of rank order of asset deployment categories by firms, intangible assets appear to have a modest edge over tangible assets for deployment by value shop firms (architecctural and engineering design firms), but these emphases are not consistent among value chain-oriented (construction) firms. 2) Although pronounced differences were expected, there was little evidence of differences in rank order of asset category accumulation and deployment by firms (according to the Delphi panel) regardless of whether the firm was focused on continuity and longevity or (alternatively) short-term profit maximization. 3) Because of their ambidexterity in production logic, the expert panel had difficulty placing EPC (Engineer - Procure - Construct), design-build and integrated services firms in a single Stabell - Fjeldstad value logic category, and a new composite category was posited based on Delphi panel feedback.