Data-driven ranking of building energy efficiency utilizing stochastic energy efficiency frontiers (SEEF)
Abstract/Contents
- Abstract
- Quantifying building energy efficiency is essential to prioritizing investments and monitoring energy efficiency programs for utilities and large-scale facility managers. Two inherent challenges of quantifying energy efficiency are (a) energy consumption is influenced by many different factors, some of which are outside the scope of influence or interest of users to change; and (b) energy consumption is highly variable, hence energy efficiency can also considerably change over time. The contribution of my research is a computational method that utilizes smart meter data to rank buildings' energy efficiency while (a) removing the effect of factors that are outside the scope of influence and interest of users to change, and (b) accounting for the fluctuation in energy consumption, by specifying a confidence interval for energy efficiency estimates. Particularly, I extend frontier methods for energy efficiency ranking by developing Stochastic Energy Efficiency Frontiers (SEEF), a method to estimate the uncertainty in efficiency rankings and validate the functional form of the frontier. I validate the power and generality of my method using three data sets in residential and commercial settings. I demonstrate how SEEF helps identify factors that drive energy efficiency, and use its result to identify interventions that contribute to improvement to energy efficiency. For instance, in residential buildings, SEEF results validate that certain efficient features such as double-pane windows are more correlated with higher efficiency ranks, while other factors such as programmable thermostats have a smaller impact on rankings. The results of my research will enable practitioners to compare energy efficiency of buildings more accurately, and to compare them over time.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kavousian, Amir Ehsan |
|---|---|
| Associated with | Stanford University, Department of Civil and Environmental Engineering. |
| Primary advisor | Fischer, Martin, 1960 July 11- |
| Primary advisor | Rajagopal, Ram |
| Thesis advisor | Fischer, Martin, 1960 July 11- |
| Thesis advisor | Rajagopal, Ram |
| Thesis advisor | Flora, June A. (June Annette) |
| Thesis advisor | Sweeney, James L |
| Advisor | Flora, June A. (June Annette) |
| Advisor | Sweeney, James L |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Amir Ehsan Kavousian. |
|---|---|
| Note | Submitted to the Department of Civil and Environmental Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Amir Ehsan Kavousian
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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