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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Author: search.filters.author.Dahlhausen, Matthew GalenSubject: search.filters.subject.Building Energy Modeling

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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.