This project conducts a data-driven feasibility analysis of wind energy production in Kenya. The goal is to identify optimal locations for wind farms, leveraging various data science techniques. The analysis includes assessing wind patterns, speeds, and potential energy output across different regions in Kenya.
- Conduct a comprehensive data-driven analysis to recommend optimal wind farm locations in Kenya.
- Analyze regional wind data to determine areas with high energy output potential.
- Implement a predictive wind gust model for enhanced operational efficiency.
- Pandas: Data manipulation and analysis.
- Cartopy: Geospatial data visualization.
- NumPy: Numerical operations on arrays.
- Xarray: Handling multi-dimensional arrays and datasets.
- Matplotlib: Plotting and visualizations.
- Streamlit: Creating interactive web applications.
- Scikit-learn (sklearn): Machine learning for predictive modeling.
- Geopandas: Geospatial data manipulation.
- Shapely: Geometric operations.
- IPython: Interactive computing and data exploration.
- TQDM: Progress bars for loops and tasks.
- Windrose: Wind rose plots for visualizing wind data.
To install the required dependencies, run the following command:
pip install pandas cartopy numpy xarray matplotlib streamlit scikit-learn geopandas shapely ipython tqdm windrose
## Usage
To run the project, use the provided scripts or commands based on the specific tasks you want to execute.
## Known Bugs
There are no known bugs at the moment.
## License
This project is licensed under the [MIT License](LICENSE.md) - see the LICENSE.md file for details.
## Acknowledgments
We would like to express our gratitude to [Heri Kimotho, Gichogu Macharia, Prudence Coredo and Abdideq Adan].