Short-term solar forecasting from all-sky images using deep learning
Abstract/Contents
- Abstract
- Integration of renewable resources, such as solar photovoltaics (PV), has been recognized as a crucial component in transition to a decarbonized energy system. However, the intermittent nature of solar power has challenged the large-scale deployment of PV. This variability is partly caused by short-term and local cloud events. Ground-based all-sky images captured with high temporal and spatial resolution have shown great promise as a source of input to forecast such fluctuations. In recent years, the development of deep learning has provided powerful tools to extract information from sky images and enhanced short-term solar forecasting capabilities. Despite these advancements, several major challenges have been identified: (1) sky image data are often imbalanced due to the tendency of installing PV systems in sunny locations; (2) cloud dynamics are not well captured by end-to-end deep solar forecasting models and uncertainty of predictions are rarely quantified; and (3) high-quality standardized sky image datasets for solar forecasting method development and benchmark are limited. In this dissertation, we explore ways to address these challenges by focusing on predicting the power output of a 30 kW roof-top PV system up to 15-minute ahead using a locally collected sky image dataset.
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 | Nie, Yuhao |
|---|---|
| Degree supervisor | Brandt, Adam |
| Thesis advisor | Brandt, Adam |
| Thesis advisor | Azevedo, Ines |
| Thesis advisor | Lobell, David |
| Degree committee member | Azevedo, Ines |
| Degree committee member | Lobell, David |
| Associated with | Stanford Doerr School of Sustainability |
| Associated with | Stanford University, Department of Energy Resources Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yuhao Nie. |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/bm790hj4850 |
Access conditions
- Copyright
- © 2023 by Yuhao Nie
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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