Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Subject: search.filters.subject.Calibration

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Now showing 1 - 6 of 6
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    DETERMINING MEASUREMENT REQUIREMENTS FOR WHOLE BUILDING ENERGY MODEL CALIBRATION
    (2020) Dahlhausen, Matthew Galen; Srebric, Jelena; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Energy retrofits of existing buildings reduce grid requirements for new generation and reduce greenhouse gas emissions. However, it is difficult to estimate energy savings, both at the individual building and entire building stock level, because building energy models are poorly calibrated to actual building performance. This uncertainty has made it difficult to prioritize research and development and incentive programs for building technologies at the utility, state, and federal level. This research seeks to make it easier to generate building energy models for existing buildings, and to calibrate buildings at the stock level, to create accurate commercial building load forecasts. Once calibrated, these building models can be used as seeds to other building energy model calibration approaches and to help utility, state, and federal actors to identify promising energy saving technologies in commercial buildings. This research details the economics of a building energy retrofit at a singular building; contributes significantly to the development of ComStock, a model of the commercial building stock in the U.S.; identifies important parameters for calibrating ComStock; and calibrates ComStock for an example utility region of Fort Collins, CO against individual commercial building interval data. A study of retrofit costs finds that measure cost and model uncertainty are the most significant sources of variation in retrofit financial performance, followed financing cost. A wide range of greenhouse gas pricing scenarios show they have little impact on the financial performance of whole building retrofits. A sensitivity analysis of ComStock model inputs across an exhaustive range of models identifies 19 parameters that explain 80 of energy use and 25 parameters that explain 90% of energy use. Building floor area alone explains 41% of energy use. Finally, a comparison of ComStock to Fort Collins, CO interval meter data shows a 6.92% normalized mean bias error and a 16.55% coefficient of variation of root mean square error based on normalized annual energy per floor area. Improvements in meter classification and ComStock model variability will further improve model fit and provide an accurate means of modeling the commercial building stock.
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    A System for 3D Shape Estimation and Texture Extraction via Structured Light
    (2010) Miller, Richard; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Shape estimation is a crucial problem in the fields of computer vision, robotics and engineering. This thesis explores a shape from structured light (SFSL) approach using a pyramidal laser projector, and the application of texture extraction. The specific SFSL system is chosen for its hardware simplicity, and efficient software. The shape estimation system is capable of estimating the 3D shape of both static and dynamic objects by relying on a fixed pattern. In order to eliminate the need for precision hardware alignment and to remove human error, novel calibration schemes were developed. In addition, selecting appropriate system geometry reduces the typical correspondence problem to that of a labeling problem. Simulations and experiments verify the effectiveness of the built system. Finally, we perform texture extraction by interpolating and resampling sparse range estimates, and subsequently flattening the 3D triangulated graph into a 2D triangulated graph via graph and manifold methods.
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    Data-Informed Calibration and Aggregation of Expert Judgment in a Bayesian Framework
    (2009) Shirazi, Calvin Homayoon; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Historically, decision-makers have used expert opinion to supplement lack of data. Expert opinion, however, is applied with much caution. This is because judgment is subjective and contains estimation error with some degree of uncertainty. The purpose of this study is to quantify the uncertainty surrounding the unknown of interest, given an expert opinion, in order to reduce the error of the estimate. This task is carried out by data-informed calibration and aggregation of expert opinion in a Bayesian framework. Additionally, this study evaluates the impact of the number of experts on the accuracy of aggregated estimate. The objective is to determine the correlation between the number of experts and the accuracy of the combined estimate in order to recommend an expert panel size.
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    EVALUATION OF A SIMULATION PLATFORM FOR ASSESSING PERFORMANCE OF PROPOSED NONBARRIER-SEPARATED MANAGED LANES
    (2009) Chen, Xiaohan; Miller-hooks, Elise; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis seeks to ascertain whether or not a chosen simulation software platform, the VISSIM simulation platform, using creative modeling methodologies for prohibiting specified user classes from using the managed lanes and ensuring realistic transitioning and weaving behavior between lanes and at managed lane access points, can provide a suitable framework for modeling and analyzing traffic operations in freeways with nonbarrier separated managed lanes. An additional goal of this thesis is to gain insight into the potential benefits in terms of performance of proposed HOT lane facility designs developed for the State of Maryland. To accomplish this, calibration of model parameters was required. The experimental design associated with the calibration took advantage of findings from results of preliminary sensitivity tests, results of numerical experiments conceived using factorial design with the intention of assessing parameter interactions, review of related literature and advice from PTV, Inc. experts.
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    A STRATEGY FOR CALIBRATING THE HSPF MODEL
    (2005-04-29) Gutierrez-Magness, Angelica Lucia; McCuen, Richard H; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The development of Total Maximum Daily Loads (TMDLs) and environmental policies rely on the application of mathematical models, both empiric and deterministic. The Hydrologic Simulation Program-FORTRAN (HSPF) is the most comprehensive model, and it is frequently applied in the development of TMDLs for nonpoint sources control. Despite the wide use of HSPF, a documented strategy for its calibration is not available. Furthermore, the most common calibration approach uses subjective fitting and focuses on the attainment of statistical goodness of fit, ignoring in many cases the rationality of the model. The goal of this research was to develop a strategy for calibrating the HSPF model in combination with the model-independent-parameter estimator (PEST). PEST is an objective parameter estimator that should eliminate some of the subjectivity from the calibration process and reduce the repetitive effort associated with subjective fitting. The strategy was established through a series of analyses, which included the development of a weighted multi-component objective function used as the criterion for calibration. The weights are a function of the flow components of the measured runoff. The use of this new weighting procedure improves model and prediction accuracy. Methods of rainfall disaggregation and their effect on the prediction accuracy were studied. The results indicated that methods based on analyses of actual storm frequency data provided the most accurate daily-disaggregated values and thus, the best conditions to achieve accurate predictions with the HSPF. Analyses showed that the HSPF model requires a start-up period of about a year to allow the predicted discharges to become insensitive to erroneous estimates of the initial storages. The predictions during the start-up period should not be used for either calibration or the analysis of the goodness of fit. Analyses also showed that using HSPF as a lumped model can reduce the prediction accuracy of discharges from a watershed with an inhomogeneous land use distribution. The fulfillment of the research objectives provides a systematic procedure that improves the hydrologic calibration of the HSPF model.
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    STATISTICAL ESTIMATION METHODS IN VOLUNTEER PANEL WEB SURVEYS
    (2004-11-17) Lee, Sunghee; Valliant, Richard; Survey Methodology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Data collected through Web surveys, in general, do not adopt traditional probability-based sample designs. Therefore, the inferential techniques used for probability samples may not be guaranteed to be correct for Web surveys without adjustment, and estimates from these surveys are likely to be biased. However, research on the statistical aspect of Web surveys is lacking relative to other aspects of Web surveys. Propensity score adjustment (PSA) has been suggested as an alternative for statistically surmounting inherent problems, namely nonrandomized sample selection, in volunteer Web surveys. However, there has been a minimal amount of evidence for its applicability and performance, and the implications are not conclusive. Moreover, PSA does not take into account problems occurring from uncertain coverage of sampling frames in volunteer panel Web surveys. This study attempted to develop alternative statistical estimation methods for volunteer Web surveys and evaluate their effectiveness in adjusting biases arising from nonrandomized selection and unequal coverage in volunteer Web surveys. Specifically, the proposed adjustment used a two-step approach. First, PSA was utilized as a method to correct for nonrandomized sample selection, and secondly calibration adjustment was used for uncertain coverage of the sampling frames. The investigation found that the proposed estimation methods showed a potential for reducing the selection and coverage bias in estimates from volunteer panel Web surveys. The combined two-step adjustment not only reduced bias but also mean square errors to a greater degree than each individual adjustment. While the findings from this study may shed some light on Web survey data utilization, there are additional areas to be considered and explored. First, the proposed adjustment decreased bias but did not completely remove it. The adjusted estimates showed a larger variability than the unadjusted ones. The adjusted estimator was no longer in the linear form, but an appropriate variance estimator has not been developed yet. Finally, naively applying the variance estimator for linear statistics highly overestimated the variance, resulting in understating the efficiency of the survey estimates.