Energy Optimizer Pro

AI-Powered Building Energy Management System

Transform building energy consumption with machine learning. Achieve 15-35% energy savings, reduce CO2 emissions by up to 25%, and reach ROI in 6-18 months.

Get Started | Documentation | API Reference

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Overview

Energy Optimizer Pro is a full-stack platform for monitoring, analyzing, and optimizing building energy consumption using advanced ML algorithms. It supports multi-building portfolios, real-time monitoring, predictive analytics, and automated optimization recommendations.

Key Capabilities

• Real-time monitoring with WebSocket-based live dashboards
• ML-powered optimization using XGBoost, LightGBM, and Random Forest
• Predictive analytics for energy usage forecasting and anomaly detection
• Executive reporting with PDF exports, cost tracking, and sustainability metrics
• Role-based access control with JWT authentication and audit logging
• Progressive Web App with mobile support and offline capability

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Architecture

graph TB
    subgraph Frontend
        A[Next.js 14] --> B[React Components]
        B --> C[Tailwind CSS]
        A --> D[PWA]
    end
    subgraph API
        E[FastAPI] --> F[JWT Auth]
        E --> G[WebSocket]
        E --> H[REST Endpoints]
    end
    subgraph ML
        I[XGBoost] --> J[Predictions]
        I --> K[Optimization]
        I --> L[Anomaly Detection]
    end
    subgraph Data
        M[PostgreSQL] --> N[Time Series]
        O[Redis] --> P[Cache / Sessions]
    end
    subgraph Monitoring
        R[Prometheus] --> S[Grafana]
        R --> T[Alertmanager]
    end
    A --> E
    E --> I
    E --> M
    E --> O
    I --> M
    E --> R

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Quick Start

Docker Compose (Recommended)

git clone https://github.com/vinsblack/energy-optimizer-pro.git
cd energy-optimizer-pro

./start.sh install
./start.sh start

The application will be available at http://localhost:3000.

Default Credentials

Role	Email	Password
Admin	admin@energy-optimizer.com	admin123
Analyst	analyst@energy-optimizer.com	analyst123
Manager	manager@energy-optimizer.com	manager123

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Manual Setup

Prerequisites

sudo apt update && sudo apt install -y \
    git curl wget \
    python3.11 python3.11-venv python3-pip \
    nodejs npm \
    docker.io docker-compose \
    postgresql-client redis-tools

Database

docker-compose up -d postgres redis

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
alembic upgrade head

Frontend

cd frontend
npm install
npm run build
npm start

Backend

cd backend
source venv/bin/activate
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Sample Data

cd backend
python scripts/seed_data.py --buildings 5 --days 30

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Technology Stack

Frontend: Next.js 14, Tailwind CSS, Recharts, Zustand, Framer Motion, PWA

Backend: FastAPI, PostgreSQL 15, Redis, Celery, Scikit-learn, XGBoost, LightGBM

Infrastructure: Docker, Nginx, Prometheus, Grafana, Alertmanager, GitHub Actions, Let's Encrypt

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System Requirements

Component	Minimum	Recommended
OS	Linux, macOS, Windows 10+ (WSL2)	Ubuntu 22.04 LTS / macOS 13+
RAM	8 GB	16 GB+
Storage	20 GB	SSD, 100 GB+
Docker	20.0+	Latest, 8 GB+ allocation
Python	3.11+	3.11+
Node.js	18.0+	18.0+

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Usage

Running Optimization via API

import requests

auth = requests.post('http://localhost:8000/auth/login', json={
    'email': 'admin@energy-optimizer.com',
    'password': 'admin123'
})
token = auth.json()['access_token']

response = requests.post(
    'http://localhost:8000/api/optimize',
    headers={'Authorization': f'Bearer {token}'},
    json={
        'building_id': 'your-building-id',
        'algorithm': 'xgboost',
        'optimization_target': 'cost_reduction'
    }
)
print(f"Job started: {response.json()['job_id']}")

Generating Reports

cd backend
python scripts/generate_report.py \
    --building-id "your-building-id" \
    --start-date "2024-07-01" \
    --end-date "2024-07-31" \
    --format pdf \
    --output "reports/july-energy-report.pdf"

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Management Commands

./start.sh install          # Full installation
./start.sh start            # Start all services
./start.sh stop             # Stop all services
./start.sh restart          # Restart services
./start.sh status           # Service status
./start.sh logs [service]   # View logs
./start.sh test             # Run test suite
./start.sh benchmark        # Performance benchmarks
./start.sh database backup  # Database backup
./start.sh database restore # Database restore

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Monitoring

./start.sh monitoring start

Service	URL	Credentials
Grafana	http://localhost:3001	admin / admin123
Prometheus	http://localhost:9090	—
Alertmanager	http://localhost:9093	—
Jaeger	http://localhost:16686	—

Performance Targets

Metric	Target	Measured
API Response Time	< 200 ms	156 ms
Dashboard Load	< 800 ms	687 ms
ML Prediction	< 100 ms	73 ms
Uptime	99.9%	99.97%

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Testing

./start.sh test                     # All tests

cd frontend && npm test             # Frontend unit tests
cd frontend && npm run test:e2e     # E2E tests

cd backend && pytest tests/ -v      # Backend unit tests
cd backend && pytest --cov=app      # Coverage report

./start.sh benchmark                # Performance benchmarks

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Security

• Multi-factor authentication (TOTP, SMS)
• AES-256 encryption for sensitive data
• Role-based access control with granular permissions
• Comprehensive audit logging
• Automated vulnerability scanning

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Roadmap

• Native mobile applications (iOS, Android)
• Multi-tenant SaaS architecture
• Expanded IoT sensor support
• LLM-powered natural language queries
• Carbon trading integration
• Multi-language support (Italian, Spanish, German, French)

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature
3. Commit changes: git commit -m 'Add feature'
4. Push: git push origin feature/your-feature
5. Open a Pull Request

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Support

• Email: vincenzo.gallo77@hotmail.com
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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License

MIT License — see LICENSE for details.

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Acknowledgments

Built with Next.js, FastAPI, PostgreSQL, Redis, XGBoost, LightGBM, Docker, Prometheus, and Grafana.

This is a portfolio/demo project. Performance metrics are based on industry benchmarks and illustrative scenarios.