SolarFarm Analysis

📄 Project Description: The SolarFarm-Analysis project is a data-driven initiative developed as part of the 10 Academy and Kifya Artificial Intelligence Mastery Program. It focuses on analyzing environmental and geographic data from Benin, Sierra Leone, and Togo to identify high-potential regions for solar energy installation.

The project was created for MoonLight Energy Solutions, a company aiming to improve operational efficiency and sustainability through strategic solar investments. Using Python 🐍 and various data analysis tools, the project processes raw environmental measurements (such as sunlight exposure, temperature, and terrain features) and transforms them into actionable insights 📊.

The analysis includes:

☀️ Evaluating solar irradiance and energy potential across regions

🌍 Mapping and visualizing environmental factors influencing solar farm placement

📈 Providing data-backed recommendations for future solar installations

Python (Pandas, NumPy, Matplotlib, Seaborn) – for data cleaning, analysis, and visualization

Jupyter Notebook – for developing and documenting analysis workflows

Geospatial Libraries (GeoPandas, Folium) – for mapping potential locations

Excel/CSV Datasets – for environmental measurement inputs

💡 Outcome: The final report provides strategic insights that support sustainable energy planning and investment decisions. It demonstrates how data analytics and AI can guide real-world renewable energy strategies for long-term environmental impact.