A framework for signal decomposition with applications to solar energy generation
Abstract/Contents
- Abstract
- We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal decomposition is carried out by minimizing the sum of losses of the components (subject to the constraints). When each loss function is the negative log-likelihood of a density for the signal component, this framework coincides with maximum a posteriori probability (MAP) estimation; but it also includes many other interesting cases. Summarizing and clarifying prior results, we give three distributed optimization methods for computing the decomposition. The signal decomposition (SD) framework has applications across many fields, but we have been motivated by problems related to large-scale data analysis for the photovoltaic (PV) power generation industry. We will demonstrate a typical example of loss-factor analysis for PV systems using the SD framework. In addition, we will discuss software implementations of both the SD modeling framework and the PV data analysis applications, both of which are published as open-source Python packages.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Meyers-Im, Bennet E |
|---|---|
| Degree supervisor | Boyd, Stephen P |
| Thesis advisor | Boyd, Stephen P |
| Thesis advisor | Brandt, Adam (Adam R.) |
| Thesis advisor | Eglash, Stephen J |
| Thesis advisor | Pilanci, Mert |
| Degree committee member | Brandt, Adam (Adam R.) |
| Degree committee member | Eglash, Stephen J |
| Degree committee member | Pilanci, Mert |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Bennet E. Meyers. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/jp927vw0049 |
Access conditions
- Copyright
- © 2023 by Bennet E Meyers-Im
- License
- This work is licensed under a Creative Commons Attribution Share Alike 3.0 Unported license (CC BY-SA).
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