An analysis of energy loads using machine learning to examine zero net energy and all-electric communities that have solar and energy storage
Abstract/Contents
- Abstract
- Twenty-first century technology is driving profound changes in the electric utility business model. At the same time, the threat of global climate change necessitates the adoption of these technologies. California and the Netherlands have been at the forefront of the adoption of solar generation and energy storage, and, for this reason, these two regions present a unique opportunity to study the adoption of these new technologies and their impact on the existing utility grid. In the research presented here, I examine two prototype communities that have integrated solar generation and energy storage. Furthermore, we apply a cutting-edge unsupervised machine learning statistical analytics platform called VISDOM-REx to gain particular insight into the differences between these communities and a standard community. By examining the net electrical consumption and production of each individual house over each hour of a year, I present a number of common electricity consumption profiles. These load profiles make it apparent when solar generation or energy storage provides a distinct advantage to the consumer or the grid operator. Certain inefficiencies in the current system are also presented and methods to overcome these are suggested. In sum, by applying machine learning to the initial data coming out of the first advanced energy communities, I have provided electric grid operators a much clearer picture of how the rollout of solar and energy storage on a much larger scale might proceed.
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 | Von Korff, Heidi Joy |
|---|---|
| Degree supervisor | Fischer, Martin, 1960 July 11- |
| Degree supervisor | Weyant, John P. (John Peter) |
| Thesis advisor | Fischer, Martin, 1960 July 11- |
| Thesis advisor | Weyant, John P. (John Peter) |
| Thesis advisor | Rajagopal, Ram |
| Degree committee member | Rajagopal, Ram |
| Associated with | Stanford University, Civil & Environmental Engineering Department. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Heidi Joy von Korff. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Heidi Joy von Korff
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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