This is a professional, recruiter-ready README.md designed to highlight your technical skills in ML and your domain knowledge in the high-growth Climate-Tech/Energy sector.
Predictive modeling for the next generation of energy infrastructure.
As the AI revolution accelerates, datacenter power consumption is projected to double by 2030. VoltVision leverages machine learning to estimate datacenter load profiles and analyze their cascading impact on the US electrical grid, enabling data-driven decisions for utility providers and sustainability officers.
- 📈 ML-Driven Demand Forecasting: Uses historical energy data and facility specifications to predict peak and average power loads with high precision.
- 🗺️ Geospatial Grid Analysis: Maps predicted demand against existing US regional grid capacity (interconnects, substations, and ISO regions).
- 🍃 Sustainability Metrics: Estimates Carbon Intensity (CI) and Power Usage Effectiveness (PUE) based on regional energy mixes.
- 📊 Impact Simulation: "What-if" scenarios for new hyperscale datacenter deployments and their stress on local transmission lines.
- 🔌 Grid Reliability Scoring: Identifies potential bottleneck regions in the US electrical infrastructure.
| Category | Tools/Technologies |
|---|---|
| Core |  |
| Data Science |    |
| Visuals/GIS |   |
| ML/Deep Learning |   |
| Energy Data | EIA API, FERC Form 714, OpenStreetMap (OSM) |
- Python 3.9+
- EIA API Key (Required for real-time grid data)
# Clone the repository
git clone https://github.com/your-username/datacenter-demand.git
cd datacenter-demand
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txtfrom voltvision.models import LoadPredictor
from voltvision.grid import GridImpactAnalyzer
# Initialize model
model = LoadPredictor.load_pretrained('models/us_regional_v1.pkl')
# Estimate demand for a 50MW IT load facility in Northern Virginia (PJM)
estimate = model.predict(it_capacity=50, region='PJM', cooling_type='liquid')
print(f"Estimated Peak Demand: {estimate.peak_mw} MW")
print(f"Grid Stress Factor: {estimate.stress_index}%")├── data/ # Processed EIA and facility datasets
├── notebooks/ # Exploratory Data Analysis & Model Training
├── src/
│ ├── models/ # ML architectures (XGBoost, LSTMs)
│ ├── grid_analysis/ # Grid capacity & constraint logic
│ └── utils/ # Geospatial and API helpers
├── tests/ # Unit tests for prediction accuracy
└── requirements.txt
- Integration with ERCOT real-time price signals.
- Support for European Grid (ENTSO-E) data.
- Computer Vision module to detect datacenter size from satellite imagery.
Contributions are what make the open-source community such an amazing place to learn, inspire, and create.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Keywords: Machine Learning, Energy Grid, Sustainability, Python, Climate-Tech, DataCenter, Demand Forecasting