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Wind Turbine Output Prediction
(Image Source:  https://lumifyenergy.com)






# Exploratory Data Analysis and Modeling the output of Wind Turbine Generators

The project included an exploratory data analysis and evaluation of regression and machine learning algorithms using Python sci-kit learn tools.  Models were trained and tested and metrics were compared to arrive at a final model.   

## Objective:
The objective of the project was to build a predictive model for Wind Turbine output.  

## Dataset source:  https://www.kaggle.com/competitions/ensimag-mmis-2024/data

### Data Contents:
- date: Self-explanatory. 
- u10: Forecast zonal wind velocity (m/s) at 10m above ground. 
- v10: Forecast meridional wind velocity (m/s) at 10m above ground. 
- u100: Forecast zonal wind velocity (m/s) at 100m above ground. 
- v100: Forecast meridional wind velocity (m/s) at 100m above ground.   
- production: Hourly-mean wind power normalised by the maximum output of the wind farm. 
  






## Authors

- [@wlouer](https://www.github.com/wlouer)


## Acknowledgements

 - The dataset was aquired for a Kaggle competition https://www.kaggle.com/competitions/ensimag-mmis-2024/data

 

## 🚀 About Me
I'm Bill—a power industry professional with 20+ years of experience in power generation. My background as a mechanical engineer led to me a role as project manager where I led the development and execution of power generation projects. These days, I'm diving into data science, visualization, and machine learning with the intention of using it as a tool to uncover insights and improve decision making in power project development, design, procurement, construction and operations.

