Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Language: English

You searched for subject:(validity AND efficiency of the electronic document). One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Georgia Tech

1. Wang, Qinpeng. Accuracy, validity and relevance of probabilistic building energy models.

Degree: PhD, Architecture, 2016, Georgia Tech

Residential and commercial buildings consume 41% of total U.S. energy consumption. Since improving energy efficiency is still the most cost efficient energy saving option in the U.S., it is not surprising that many new buildings represent a push towards ultra-efficiency. Many studies argue that this calls for the use of high fidelity prediction models that by necessity will be probabilistic in nature due to many sources of uncertainty that affect the translation of a design specification into the actual reality of a constructed and operated facility. To inspect the fidelity of these probabilistic models against traditional deterministic models, we pose questions that address three major aspects of this new generation of building energy models: • Accuracy: do these models give more “correct” answers? • Validity: do these models lead to “better” design/retrofit decisions? • Relevance: does a profession that deploys these models provide “higher” value to the industry? This dissertation addresses the first question by identifying gaps in our understanding and quantifying various sources of model uncertainty reported in recent literature. Insufficiently understood and not well-quantified sources are further studied and resolved. The results of the above are analyzed in a sensitivity analysis that ranks input parameters alongside with model form uncertainties. Next, we adapt proven methods to conduct verification of probabilistic building energy models. Probabilistic calibration, marginal calibration and a continuous rank probability score are used to evaluate the “correctness” of the new generation of models. We illustrate the challenges of delivering validity proofs in a case study where outcomes of uncertainty analysis are translated into (monetary) risks and their influence is analyzed in a decision-making scenario involving energy performance contracts. Lastly, the study introduces a speculative approach to proving relevance by quantifying the overall societal benefit of a transparent risk framework that has the potential to unlock currently stagnating capital flow into large-scale building retrofits. Advisors/Committee Members: Augenbroe, Godfried (advisor), Wu, Jeff (committee member), Paredis, Chris (committee member), De Wilde, Pieter (committee member), Brown, Jason (committee member).

Subjects/Keywords: Accuracy; Validity; Relevance; Building energy efficiency

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, Q. (2016). Accuracy, validity and relevance of probabilistic building energy models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55645

Chicago Manual of Style (16th Edition):

Wang, Qinpeng. “Accuracy, validity and relevance of probabilistic building energy models.” 2016. Doctoral Dissertation, Georgia Tech. Accessed January 23, 2021. http://hdl.handle.net/1853/55645.

MLA Handbook (7th Edition):

Wang, Qinpeng. “Accuracy, validity and relevance of probabilistic building energy models.” 2016. Web. 23 Jan 2021.

Vancouver:

Wang Q. Accuracy, validity and relevance of probabilistic building energy models. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/1853/55645.

Council of Science Editors:

Wang Q. Accuracy, validity and relevance of probabilistic building energy models. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55645

.