Standardized Computational Framework For Prototypical Building Energy Model Creation and Building Energy Analyses

Ye, Yunyang

Graduate Thesis Or Dissertation

Standardized Computational Framework For Prototypical Building Energy Model Creation and Building Energy Analyses Public Deposited

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/hq37vp65h

Abstract

Past research has demonstrated that high-efficiency building technologies have great potential to reduce the energy consumption of commercial buildings. However, different technologies may have different impacts on various types of commercial buildings depending on the climates. Thus, it is necessary to conduct building energy analyses to identify areas where energy efficiency can be improved for different types of commercial buildings. Despite of the process, current research has three limitations: (1) Current research is often done in ad-hoc fashion and requires lengthy computing time. A standardized computational framework to streamline and accelerate building energy analyses is needed. (2) Prototypical building energy models represent the standard or reference energy models for the most common commercial buildings. They are often used as the starting point in conducting building energy analyses. However, current prototypical building energy models only represent limited types of buildings in certain countries, which limits their applications. (3) To select energy efficiency measures (EEMs), researchers tend to apply static energy prices to estimate their return on investment (ROI). Recently, more and more commercial buildings are adopting dynamic electricity pricing programs and the ROI analyses based on static energy prices may not be valid anymore. However, the impacts of dynamic electricity pricing programs on the selection of EEMs has not been fully evaluated.

To address the above three limitations, this dissertation creates a standardized computational framework for U.S. commercial buildings, applies it to create new prototypical models, and analyzes the impact of dynamic electricity pricing using these models. First, this dissertation reviews existing energy-related data sources for U.S. commercial buildings. These sources include nine building energy databases in total, three from surveys and six from simulations. Their applications are detailed for building energy analyses. Based on the review, a standardized computational framework for U.S. commercial buildings is created, which can select the best data sources and methods to create prototypical building energy models and conduct building energy analyses.

Then, by using the framework, this dissertation proposes a new methodology for prototypical building energy model creation independent of building types and countries. By using thisnew methodology, this dissertation creates prototypical building energy models for four types of U.S. commercial buildings: (1) medium office buildings, (2) religious worship buildings, (3) college/university buildings, and (4) mechanical shops. The medium office buildings and religious worship buildings are used as two case studies.Finally, this dissertation uses the framework to analyze the impacts of electricity pricing programs on the selection of EEMs. The DOE Commercial Prototype Building Energy Models for medium office buildings are the baseline models in these analyses. Furthermore, this research involves three global sensitivity analysis methods and five electricity pricing programs. The results by only considering building energy savings are similar to those of other studies. Moving on to the cost analysis, the results indicate that the ROIs of EEMs greatly change under different electricity pricing programs. If different electricity pricing programs were available to commercial buildings, building owners would be more likely to conduct energy retrofits to take advantage of these savings.

This dissertation addresses the three limitations: (1) create a standardized computational framework for U.S. commercial buildings regulates the analysis process and automatizes the whole procedure, (2) develop a new methodology for prototypical building energy model creation, and (3) provide a new perspective about the selection of EEMs by considering the impact of dynamic pricing programs. The future research will extend the scope of the standardized computational framework, complement the sets of prototypical building energy models, and continue researching on the impact of dynamic electricity pricing programs.

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