#  MoonLight Energy Solutions – data analysis

This repository contains an exploratory data analysis (EDA) pipeline for environmental sensor data collected from **Benin**, **Sierra Leone**, and **Togo**.

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##  Project Objective

As an analytics engineer at **MoonLight Energy Solutions**, the goal is to:
- Clean and explore environmental sensor data
- Detect anomalies, trends, and correlations
- Compare solar performance across the three countries
- Recommend optimal regions for solar installation based on insights

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##  Project Structure

MoonLight-Energy-Solutions-Data-Analysis/ ├── notebooks/ -- benin.ipynb -- compair_countries.ipynb -- sierraleone.ipynb -- togo.ipynb |── scripts/ ├── data/ ├── cleaned_data/ ├── test_data/ -- benin-malanville.csv -- sierraleone-bumbuna.csv -- togo-dapaong_qc.csv |── graphs/ -- benin/ -- sierraleone/ -- togo/ ├── requirements.txt ├── .gitignore ├── setup.sh └── README.md

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## 🔍 Analysis Highlights

Each country's EDA notebook includes:
- Summary statistics & missing value report
- Outlier detection and cleaning (Z-score method)
- Time-series plots (daily & hourly GHI, Tamb)
- Boxplots, histograms, and heatmaps
- Wind rose plots and scatter plots
- Bubble charts (GHI vs Tamb with RH as size)
- Impact analysis of manual cleaning on panel performance

>  Raw datasets are excluded from the repo via `.gitignore`. `test_data/` contains mock CSVs for public testing and CI/CD.

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##  Version Control with Git

This project was version-controlled using **Git** throughout the process:
- All milestones were committed progressively (setup, cleaning, EDA, documentation)
- GitHub was used for remote version control and collaboration
- CI/CD integration via GitHub Actions automatically validates notebooks

 View full commit history:  
[GitHub commit log](https://github.com/NuryeNigusMekonen/MoonLight-Energy-Solutions-Data-Analysis/commits/main)

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##  Environment Setup Instructions

You can use the included `setup.sh` or follow these steps manually:

### 1. Clone the repository

```bash
git clone https://github.com/NuryeNigusMekonen/MoonLight-Energy-Solutions-Data-Analysis.git
cd MoonLight-Energy-Solutions-Data-Analysis

2. Create and activate a virtual environment

python -m venv week_0_venv
source week_0_venv/bin/activate  # Windows: .\week_0_venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run notebooks

Use Jupyter or VSCode to open and run the notebooks in the notebooks/ directory.

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Optional: Quick Setup via Bash Script

You can also use the automated setup script:

bash setup.sh

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Key Insights

• Benin: Strong midday irradiance, moderate wind variability. Cleaning slightly improved ModA/ModB performance.
• Sierra Leone: Higher humidity and cloud cover reduced direct irradiance (DNI).
• Togo: Most balanced solar conditions and fewer anomalies — strong candidate for stable output.

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Tools Used

• Python, Pandas, NumPy
• Seaborn, Matplotlib, Plotly
• Jupyter Notebooks
• Git, GitHub, GitHub Actions (CI/CD)
• Windrose library

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Files Not Included

• Raw .csv files in data/ and cleaned_data/ are excluded via .gitignore
• Use mock files from test_data/ or add your own local data to run full EDA

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Sample EDA Results

GHI vs Ambient Temperature (Bubble Size = RH)

This bubble chart shows GHI generally increases with temperature, with larger RH values associated with reduced irradiance.

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Wind Rose Plot (Benin)

Visualizes wind direction and speed distributions in Malanville.

average ghi image

comparing all contries

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Continuous Integration & Test Data Handling

The notebooks automatically switch to test_data/ if CI=true is detected in the environment (used in GitHub Actions).
Data loading is modularized with error handling via a load_data() function, which improves reproducibility and CI compatibility.

## 👤 Author & Credits

**Nurye Nigus Mekonen**
Electrical & Software Engineer
📧 [nurye.nigus.me@gmail.com](mailto:nurye.nigus.me@gmail.com)
🔗 [LinkedIn](https://www.linkedin.com/in/nryngs/)
🐙 [GitHub](https://github.com/NuryeNigusMekonen)
📞 +251 929 404 324

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