SolarEnergyPrediction

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SolarEnergyPrediction

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Use historical energy production values along with weather predictions to forecast photovoltaic energy production. Uses gradient boosting (XGBoost) for predictions.

SolarEnergyPrediction 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# SolarEnergyPrediction

Use historical energy production values along with weather predictions to forecast photovoltaic energy production.

## To run this code, you must have installed

### Machine learning packages

* xgboost          (pip install xgboost)
* graphviz         (pip install graphviz)

### Plotting packages

* matplotlib       (pip install matplotlib)
* seaborn          (pip install seaborn)

### General computing packages

* pandas           (pip install pandas)
* numpy            (pip install numpy)
* pytz             (pip install pytz)

### Webscraping packages

* beautiful soup 4 (pip install beautifulsoup4)
* selenium         (pip install selenium)

### Compiled pip install command

The full command is `pip install xgboost graphviz beautifulsoup4 selenium pandas numpy matplotlib seaborn pytz`.

## Running this code

The `model.py` file contains the creation, testing, and plotting of the xgboosted model.
The `scrape_website.py` file is for collecting the solar energy output data.

For an easy to run script, run the `src/model.py` file while in the project directory.

## Git Large File Storage

Git LFS is used to store the large weather data files. Install it before downloading these large files. View the 'Installing Git LFS' section on this website <https://www.atlassian.com/git/tutorials/git-lfs> to see instructions.

## Known issues

The only issue is with plotting. The graphs sometimes may be scaled strangely or not apply settings, so to fix it, you must only make one plot/graph at a time. I have resolved the issue, but if it happens to not be fixed for your own plots, then this is how to fix it.

SolarEnergyPrediction

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