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

Category: Renewable Energy
Sub Category: Photovoltaics and Solar Energy

Keywords

data-processing machine-learning matlab neural-network python tensorflow

Last synced: about 14 hours ago
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Repository metadata

Predict the Power Production of a solar panel farm from Weather Measurements using Machine Learning

• Host: GitHub
• URL: https://github.com/ColasGael/Machine-Learning-for-Solar-Energy-Prediction
• Owner: ColasGael
• License: mit
• Created: 2018-05-06T19:43:04.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-11-07T18:37:29.000Z (over 5 years ago)
• Last Synced: 2025-04-25T11:41:11.670Z (1 day ago)
• Topics: data-processing, machine-learning, matlab, neural-network, python, tensorflow
• Language: Python
• Homepage:
• Size: 922 MB
• Stars: 264
• Watchers: 13
• Forks: 111
• Open Issues: 1
• Releases: 0

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.

        

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Owner metadata

• Name: Gael Colas
• Login: ColasGael
• Email:
• Kind: user
• Description: Master of Science French graduate in Aero/Astro at Stanford
• Website:
• Location: Stanford
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/24507878?u=e16722fa897c00b7b0cfe0277b0c7e02f026f3de&v=4
• Repositories: 16
• Last ynced at: 2024-06-11T15:42:29.329Z
• Profile URL: https://github.com/ColasGael

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GitHub Events

Total

• Issues event: 1
• Watch event: 32
• Pull request event: 3
• Fork event: 7

Last Year

• Issues event: 1
• Watch event: 32
• Pull request event: 3
• Fork event: 7

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Committers metadata

Last synced: 6 days ago

Name	Email	Commits
Gael Colas	c****g@s****u	9
Alexander McKeehan	a****n@A****l	2

Committer domains:

• stanford.edu: 1

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Issue and Pull Request metadata

Last synced: 1 day ago

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/ColasGael/Machine-Learning-for-Solar-Energy-Prediction

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Score: 6.272877006546167