Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/121676 |
| _version_ | 1826215891477463040 |
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| author | Lauer, Michelle(Michelle F.) |
| author2 | Marija Ilic. |
| author_facet | Marija Ilic. Lauer, Michelle(Michelle F.) |
| author_sort | Lauer, Michelle(Michelle F.) |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T16:38:41Z |
| format | Thesis |
| id | mit-1721.1/121676 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:38:41Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1216762019-07-24T03:08:10Z Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid Lauer, Michelle(Michelle F.) Marija Ilic. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 91-94). In the current era of Internet of Things (IoT) devices, household solar panels, and increasingly aordable local energy storage, energy grid systems are facing a new set of challenges that they were not originally designed to support. Energy systems of the near future must be capable of supporting these new technologies, but new technology can also be leveraged to improve reliability and eciency overall. A major source of potential improvements comes from the increase of connected devices that are capable of dynamically adjusting their behavior, and offer new data that can be used for optimization and prediction. Energy predictions are used today at the bulk power system level to ensure demand is met through appropriate resource allocation. As energy systems become more responsive, prediction will be important at more granular system levels and timescales. Enabled by the rise in available data, existing research has shown some machine learning models to be superior to traditional statistical models in predicting long-term aggregate usage. However, these models tend to be computationally expensive; if machine learning prediction models are to be used at short timescales and performed close to the end nodes, there is a need for more ecient models. Additionally, most machine learning models today do not take advantage of the known and studied properties of the underlying energy data. This thesis explores the circumstances under which machine learning can be used to make predictions more accurately than existing methods, and how machine learning and statistical methods can serve to complement each other (specically for short timescales at the household level). We nd that basic machine learning models outperform other baseline and statistical models by using energy usage trends observed from statistical methods to better engineer the input features. For the increasingly distributed energy systems that these predictive models aim to support, the distributed nature of blockchain technology has been proposed as a good match for managing such systems. As an example of one possible distributed management implementation, this thesis presents a novel blockchain-enabled architecture that provides privacy for users, information security through improved household-level prediction, and takes into consideration the security vulnerabilities and computational constraints of the participants. by Michelle Lauer. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-07-15T20:33:13Z 2019-07-15T20:33:13Z 2019 2019 Thesis https://hdl.handle.net/1721.1/121676 1102056867 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 94 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Lauer, Michelle(Michelle F.) Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid |
| title | Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid |
| title_full | Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid |
| title_fullStr | Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid |
| title_full_unstemmed | Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid |
| title_short | Real-time household energy prediction : approaches and applications for a blockchain-backed smart grid |
| title_sort | real time household energy prediction approaches and applications for a blockchain backed smart grid |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/121676 |
| work_keys_str_mv | AT lauermichellemichellef realtimehouseholdenergypredictionapproachesandapplicationsforablockchainbackedsmartgrid |