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- Electricity use analysis of existing and planned university...
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UBC Theses and Dissertations
Electricity use analysis of existing and planned university buildings, and opportunities for life cycle costing Glaser, Leonard
Abstract
Increasing environmental awareness has initiated a change in building design and efficiency in order to reduce the large amount of energy associated with this industry. Life cycle cost (LCC) analysis is a decision-making tool to evaluate the economic long-term benefits of different design options compared to the building’s basic design. LCC analysis can motivate decision-makers to reallocate building budgets towards higher initial capital costs if the long-term operational savings balance higher upfront expenses. However, LCC needs well-calibrated predictive modeling for such savings to be realized. ‘Bottom up’ Energy modeling software has been used to evaluate savings associated with different building designs. Although these models require a large amount of building specific information, their predictions are often far off from the actual energy use. An alternative proposed in this thesis is to use ‘top down’ models that predict energy consumption using aggregate building characteristics such as size, age, type and occupancy. We have developed a ‘top-down’ model for electricity use in buildings based on daily electricity consumption data of 48 research buildings at the University of British Columbia (UBC). The model is a set of linear regressions analyzed with MATLAB. Our model requires only a few simple, aggregate inputs in order to make electricity use predictions. These compare favorably to the more complex LEED energy tested models for ten UBC research buildings. Thus, the ‘top down’ models are an additional, useful tool for energy planning and design. The effort to collect data for such models is also small compared to the ‘bottom-up’ alternative.
Item Metadata
Title |
Electricity use analysis of existing and planned university buildings, and opportunities for life cycle costing
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2016
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Description |
Increasing environmental awareness has initiated a change in building design and efficiency in order to reduce the large amount of energy associated with this industry. Life cycle cost (LCC) analysis is a decision-making tool to evaluate the economic long-term benefits of different design options compared to the building’s basic design. LCC analysis can motivate decision-makers to reallocate building budgets towards higher initial capital costs if the long-term operational savings balance higher upfront expenses. However, LCC needs well-calibrated predictive modeling for such savings to be realized. ‘Bottom up’ Energy modeling software has been used to evaluate savings associated with different building designs. Although these models require a large amount of building specific information, their predictions are often far off from the actual energy use. An alternative proposed in this thesis is to use ‘top down’ models that predict energy consumption using aggregate building characteristics such as size, age, type and occupancy. We have developed a ‘top-down’ model for electricity use in buildings based on daily electricity consumption data of 48 research buildings at the University of British Columbia (UBC). The model is a set of linear regressions analyzed with MATLAB. Our model requires only a few simple, aggregate inputs in order to make electricity use predictions. These compare favorably to the more complex LEED energy tested models for ten UBC research buildings. Thus, the ‘top down’ models are an additional, useful tool for energy planning and design. The effort to collect data for such models is also small compared to the ‘bottom-up’ alternative.
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Genre | |
Type | |
Language |
eng
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Date Available |
2017-01-21
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0340482
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2017-02
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International