The application of Bayesian inference to identify the energy saving potential for energy efficiency measures through electric utility interval data and weather information
Abstract/Contents
- Abstract
- Utility ratepayer-funded energy efficiency programs are the prevalent method for reducing the energy consumption of single-family homes. These programs rely on estimating the electric energy efficiency savings potential of various EEMs (e.g., added insulation or weatherization) to determine their cost-effectiveness, which is the main criterion for determining the success of a program. Program managers lack a cost efficient and evidence-driven method to infer the energy savings potential of a specific energy efficiency measure in a single-family home. This dissertation presents a cost-efficient and empirical evidence-driven method for inferring the energy savings potential of an energy efficiency measure by analyzing moments when the inefficiency of the relevant characteristics is most prominent. This Inefficiency Estimation Method selects homes using only readily available information, specifically 15-minute electric utility data (i.e., interval data) and hourly ambient weather data. The cornerstone of this method is the application of an objective Bayesian model. The use of Bayesian statistics has several benefits over classical statistical methods. One benefit is the ability to improve the accuracy of the analysis by adding new information into the model through informed objective priors. I compared the results of my Inefficiency Estimation Method to selecting homes based on annual energy consumption and selecting homes based on annual A/C energy usage, the currently available empirical methods. The comparative metrics for the methods were energy savings and cost-effectiveness. The inefficiency estimation method calculated 2X more energy savings and 30% better cost-effectiveness for A/C unit replacement than the existing methods. For selecting homes that need insulation, the inefficiency estimation method calculated 18% more energy savings and approximately the same cost-effectiveness as compared to the A/C energy usage method. These results show that it is feasible to target homes by inferring the energy savings potential of specific energy efficiency measures with interval data and weather information using Bayesian inference. This selection method provides energy efficiency program managers an empirical evidence-based way for household level targeting that has the potential to increase such programs' energy savings and cost-effectiveness. Future work can investigate the ability of the Inefficiency Estimation Method to infer the energy savings potential of other energy efficiency measures.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Kinslow, Anthony Dylan |
|---|---|
| Degree supervisor | Fischer, Martin, 1960 July 11- |
| Thesis advisor | Fischer, Martin, 1960 July 11- |
| Thesis advisor | Jain, Rishee |
| Thesis advisor | Rajagopal, Ram |
| Degree committee member | Jain, Rishee |
| Degree committee member | Rajagopal, Ram |
| Associated with | Stanford University, Civil & Environmental Engineering Department. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Anthony Dylan Kinslow II. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Anthony Dylan Kinslow
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