UTL000008_WTG_Ensimag

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

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.