Data mining for demand management : segmentation, targeting, and analytics visualization
Abstract/Contents
- Abstract
- As power generation gets more distributed, and electricity markets become more deregulated throughout the world, utilities have been trying to find a way to match consumption with generation. Recently, advanced metering infrastructure (AMI) has been deployed wide-spread including smart meters, and smart meters make two-way communication possible between the meter and the central system. They provide concrete information about customer electricity consumption and create a unique opportunity to investigate and understand customer consumption behavior much better than before. Integrating the needs of utilities and potential benefits to customers, the ultimate target of the studies in this dissertation is building a data-driven demand management system which is based on smart meter data analytics. Thus, we develop scalable methodologies of data analytics using data mining, machine learning techniques. The methodologies developed in this dissertation include learning customer consumption patterns, segmenting customers by relevant features, selecting the proper customers for various energy programs and implementing a data analytics system.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kwac, Jungsuk |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Rajagopal, Ram |
| Thesis advisor | Rajagopal, Ram |
| Thesis advisor | El Gamal, Abbas A |
| Thesis advisor | Fischer, Martin, 1960 July 11- |
| Advisor | El Gamal, Abbas A |
| Advisor | Fischer, Martin, 1960 July 11- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jungsuk Kwac. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Jung Suk Kwac
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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