Machine-Learning-for-Solar-Energy-Prediction
Predict the Power Production of a solar panel farm from Weather Measurements using Machine Learning
https://github.com/ColasGael/Machine-Learning-for-Solar-Energy-Prediction
Science Score: 10.0%
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○CITATION.cff file
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✓Committers with academic emails
1 of 2 committers (50.0%) from academic institutions -
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Low similarity (7.0%) to scientific vocabulary
Keywords
data-processing
machine-learning
matlab
neural-network
python
tensorflow
Last synced: 6 months ago
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Repository
Predict the Power Production of a solar panel farm from Weather Measurements using Machine Learning
Basic Info
Statistics
- Stars: 271
- Watchers: 12
- Forks: 114
- Open Issues: 1
- Releases: 0
Topics
data-processing
machine-learning
matlab
neural-network
python
tensorflow
Created almost 8 years ago
· Last pushed over 6 years ago
https://github.com/ColasGael/Machine-Learning-for-Solar-Energy-Prediction/blob/master/
# Machine-Learning-for-Solar-Energy-Prediction by Adele Kuzmiakova, Gael Colas and Alex McKeehan, graduate students from Stanford University This is our final project for the CS229: "Machine Learning" class in Stanford (2017). Our teachers were Pr. Andrew Ng and Pr. Dan Boneh. Language: Python, Matlab, R Goal: predict the hourly power production of a photovoltaic power station from the measurements of a set of weather features. This project could be decomposed in 3 parts: - Data Pre-processing: we processed the raw weather data files (input) from the National Oceanographic and Atmospheric Administration and the power production data files (output) from Urbana-Champaign solar farm to get meaningful numeric values on an hourly basis ; - Feature Selection: we run correlation analysis between the weather features and the energy output to discard useless features, we also implemented Principal Component Analysis to reduce the dimension of our dataset ; - Machine Learning : we compared the performances of our ML algorithms. Implemented models include Weighted Linear Regression with and without dimension reduction, Boosting Regression Trees, and artificial Neural Networks with and without vanishing temporal gradient Our final report and poster are available at the root.
Owner
- Name: Gael Colas
- Login: ColasGael
- Kind: user
- Location: Stanford
- Repositories: 16
- Profile: https://github.com/ColasGael
Master of Science French graduate in Aero/Astro at Stanford
GitHub Events
Total
- Issues event: 1
- Watch event: 39
- Pull request event: 3
- Fork event: 10
Last Year
- Issues event: 1
- Watch event: 39
- Pull request event: 3
- Fork event: 10
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Gael Colas | c****g@s****u | 9 |
| Alexander McKeehan | a****n@A****l | 2 |
Committer Domains (Top 20 + Academic)
stanford.edu: 1
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 4
- Total pull requests: 4
- Average time to close issues: 7 months
- Average time to close pull requests: 4 months
- Total issue authors: 3
- Total pull request authors: 2
- Average comments per issue: 2.5
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 4
- Average time to close issues: N/A
- Average time to close pull requests: 4 months
- Issue authors: 0
- Pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- joonv2 (2)
- oliver021 (1)
- sethubhargavmeruga (1)
Pull Request Authors
- saadabdullah-15 (2)
- Moosa-Anwar-Khan (2)