MoonLight Energy Solutions - Solar Investment Strategy Analysis

Overview

MoonLight Energy Solutions is dedicated to enhancing operational efficiency and sustainability through targeted investments in solar energy. This project focuses on analyzing environmental measurements provided by the engineering team to uncover trends and generate actionable insights for strategic solar installations.

As an Analytics Engineer, the primary objective of this analysis is to:

• Perform statistical analysis and exploratory data analysis (EDA).
• Identify high-potential regions for solar installations.
• Provide data-driven recommendations aligned with the company's long-term sustainability goals.

---

Project Objectives

1. Environmental Data Analysis

   • Analyze the environmental measurements provided by the engineering team.
   • Identify trends, anomalies, and significant patterns in the data.

2. Strategic Insights

   • Translate analysis into meaningful insights that support MoonLight Energy Solutions' objectives.
   • Highlight regions with the highest potential for solar installations.

3. Recommendation Report

   • Deliver a strategy report summarizing findings and actionable recommendations.
   • Align recommendations with long-term sustainability and operational goals.

---

Key Deliverables

1. Exploratory Data Analysis (EDA)

   • Visualizations and summaries showcasing key trends and correlations in the data.
   • Statistical analysis to validate observations and uncover insights.

2. Strategy Report

   • Comprehensive report detailing identified high-potential regions.
   • Data-driven recommendations to guide solar investment strategies.

3. Code and Documentation

   • Reproducible code for data cleaning, analysis, and visualization.
   • Detailed documentation to ensure clarity and usability.

---

Workflow

1. Data Preparation

   • Clean and preprocess the provided environmental measurement data.
   • Address missing values, outliers, and inconsistencies.

2. Exploratory Analysis

   • Conduct EDA to identify trends and patterns.
   • Use statistical tools and visualizations to support analysis.

3. Insight Generation

   • Synthesize findings into actionable insights.
   • Prioritize regions based on solar installation potential.

4. Report Creation

   • Compile observations and recommendations into a professional strategy report.

---

Technologies Used

• Python: For data analysis, statistical computations, and visualization.
• Pandas & NumPy: Data manipulation and preprocessing.
• Matplotlib & Seaborn: Visualization of trends and patterns.
• Jupyter Notebook: Interactive analysis and reporting.

---

Getting Started

1. Clone this repository:

   git clone https://github.com/Azazh/solar-power-analytics.git

2. Install required dependencies:

   pip install -r requirements.txt

3. Run the analysis notebook:

   jupyter notebook benin-malanville.ipynb
   jupyter notebook eda_combined_data_sierra_leone_togo_benin.ipynb.ipynb 
   jupyter notebook sierraleone-bumbuna.ipynb
   jupyter notebook togo-dapaong_qc.ipynb

---

Contributing

Contributions are welcome! Please follow these steps:

1. Fork this repository.
2. Create a feature branch (git checkout -b feature-branch).
3. Commit changes (git commit -m "Add your feature").
4. Push to the branch (git push origin feature-branch).
5. Submit a pull request.

Acknowledgments

We extend our gratitude to the engineering team for providing critical environmental measurement data that drives this analysis.