NetZero is a hybrid Edge–Cloud Software-as-a-Service (SaaS) platform that bridges the gap between thermodynamic building modelling and real-time electricity grid performance. By combining machine learning-based thermal load prediction with live carbon intensity forecasts, the platform enables carbon-aware operational scheduling that reduces energy costs, lowers carbon emissions, and improves sustainability outcomes.
Buildings often consume energy without considering the carbon intensity of the electricity supplying the grid. This leads to:
- Inefficient operation of HVAC and other flexible assets.
- The Demand Trap, where energy-intensive devices operate during periods of high carbon intensity.
- Increased operational costs.
- Difficulty meeting sustainability and ESG targets.
NetZero addresses these challenges by integrating physical building characteristics with real-time and forecasted grid carbon intensity, enabling intelligent load shifting, automated decision-making, and carbon-aware operational planning.
Reduce inefficient HVAC cycling by leveraging thermodynamic predictions to maintain comfortable indoor temperatures while minimizing energy consumption through informed ventilation and shading strategies.
Reduce operational carbon emissions by scheduling flexible energy-intensive activities, such as pre-heating equipment or refrigeration cycles, during periods of lower grid carbon intensity.
Achieve organizational sustainability and ESG objectives through automated threshold-based load modulation that dynamically adjusts non-critical assets during carbon-intensive periods.
- Thermodynamic Digital Twin Onboarding
- Building Profile Management
- Flexible Asset Registration
- Regional Carbon Forecast Ingestion
- Carbon-Aware Operational Scheduling
- Predictive Grid Intensity Analytics
- Automated Threshold-Based Load Modulation
- Portfolio Dashboard
- ESG Reporting
- Retrofit Simulation
- Sustainability Report Generation
- Email Notifications
- Authentication and User Management
- Next.js
- TypeScript
- Tailwind CSS
- shadcn/ui
- Recharts
- Python
- Django REST Framework
- PyTorch
- CoreML
- PostgreSQL (NeonDB)
- Render
- Vercel
- GitHub Actions (CI/CD)
Used to train machine learning models that predict heating and cooling loads using eight structural building parameters.
Repository: https://archive.ics.uci.edu/ml/datasets/Energy+efficiency
- Relative Compactness
- Surface Area
- Wall Area
- Roof Area
- Overall Height
- Orientation
- Glazing Area
- Glazing Area Distribution
Outputs:
- Heating Load
- Cooling Load
Used to retrieve real-time and forecast carbon intensity information for regional electricity grids, enabling carbon-aware scheduling and automated energy management.
https://carbonintensity.org.uk/
+----------------------+
| Next.js UI |
| Dashboard & Forms |
+----------+-----------+
|
|
Django REST Framework
|
+----------------------+----------------------+
| | |
| | |
Building Services ML Inference Engine Carbon Forecast Engine
| | |
+----------------------+----------------------+
|
PostgreSQL (NeonDB)
|
Scheduled Tasks & Automation
git clone https://github.com/dr1810/NetZero.git
cd NetZeroOpen a new terminal.
cd netzero-frontend
npm install
npm run devThe frontend will normally be available at:
http://localhost:3000
Open a second terminal.
cd netzero-backend
pip install -r requirements.txt
python manage.py migrate
python manage.py runserverThe backend will normally be available at:
http://127.0.0.1:8000
Navigate to
http://localhost:3000
Every push and pull request automatically triggers the GitHub Actions pipeline.
The pipeline performs:
- Backend validation
- Frontend build verification
- Unit testing
- Integration testing
- Dependency installation
- Continuous deployment readiness checks
Pipeline status:
https://github.com/dr1810/NetZero/actions
NetZero
│
├── netzero-frontend
│ ├── app
│ ├── components
│ ├── lib
│ ├── public
│ └── styles
│
├── netzero-backend
│ ├── api
│ ├── config
│ ├── services
│ ├── models
│ ├── migrations
│ └── tests
│
├── .github
│ └── workflows
│
├── docs
├── README.md
└── LICENSE
The platform predicts a building's thermal behaviour using a neural network trained on the UCI Energy Efficiency Dataset.
The prediction engine estimates:
- Heating Load
- Cooling Load
These predictions are combined with live regional carbon forecasts to generate optimized operational schedules.
The optimization engine combines:
- Building thermodynamic characteristics
- Predicted heating and cooling demand
- Regional carbon intensity forecasts
- User-defined sustainability preferences
The system then:
- Identifies optimal operating windows
- Generates carbon-aware schedules
- Recommends low-carbon operating periods
- Automatically modulates eligible assets when thresholds are exceeded
The project includes:
- Backend API Tests
- Integration Tests
- Machine Learning Validation
- Frontend Validation
- GitHub Actions CI Pipeline
https://net-zero-black.vercel.app/
https://netzero-jonf.onrender.com/
GitHub Repository
https://github.com/dr1810/NetZero
Latest Release
https://github.com/dr1810/NetZero/releases/tag/v1.0.0
Future development of NetZero will focus on:
- Advanced reinforcement learning for adaptive scheduling
- Occupancy-aware optimization
- Smart home and IoT device integration
- Battery and renewable energy optimization
- Mobile application support
- Multi-region carbon forecasting
- Enhanced ESG analytics
- Explainable AI for scheduling decisions
Dharun Ramesh
New York University
M.S. Computer Science
GitHub: https://github.com/dr1810
This project was developed for academic purposes as part of the Software Engineering I course at New York University.