Smart Energy Lader - AI Factory Optimization

⚡ Smart Energy Lader

AI-Powered Energy Optimization for Industrial Automation

About • Features • Quick Start • Documentation

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🏭 About

Smart Energy Lader is an advanced AI-powered energy optimization system designed for Factory I/O industrial automation environments. It uses Reinforcement Learning (RL) with PPO (Proximal Policy Optimization) to intelligently optimize energy consumption while maintaining production throughput and system efficiency.

Key Statistics

• 📊 Energy Reduction: 18-25% vs baseline operations
• ⚡ Peak Power Reduction: 45% vs unoptimized spikes
• 🎯 Throughput Maintained: ≥95% of baseline
• 🚀 Real-time Decisions: <100ms AI response time

Factory Components

• 14 Belt Conveyors
• 4 Pneumatic Pushers
• 3 Two-Axis Pick & Place Robots
• 7 Vision Sensors
• 2 Positioners

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✨ Features

🤖 AI Optimization

• Reinforcement Learning: PPO agent trained on 100,000+ timesteps
• Hybrid Actions: Discrete machine sequencing decisions
• Adaptive Learning: Continuous improvement from operational data

📊 Real-Time Dashboard

• Live power consumption monitoring
• Interactive factory layout visualization
• AI confidence scores and decision explanations
• Manual override capabilities
• Performance metrics and KPIs

📈 Comprehensive Analytics

• Statistical comparison (baseline vs optimized)
• Performance trend analysis
• Energy savings reports
• Cost-benefit analysis

🔧 Industrial Integration

• Modbus TCP Support: Direct Factory I/O communication
• LabVIEW Compatible: JSON/CSV data exchange
• SCADA Ready: Export decision data to existing systems

🧪 Development Tools

• 4 Jupyter Notebooks for complete pipeline
• Streamlit dashboard for live monitoring
• Comprehensive unit tests
• GitHub Actions CI/CD pipeline

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🚀 Quick Start

Prerequisites

Python 3.9+
Git
Virtual environment

Installation

1. Clone Repository

git clone https://github.com/your-username/smart-energy-lader.git
cd smart-energy-lader

2. Create Virtual Environment

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

3. Install Dependencies

pip install -r requirements.txt
pip install -r dashboard/requirements.txt

4. Verify Installation

python -c "import gym; import stable_baselines3; import streamlit; print('✓ Ready!')"

Run Dashboard

streamlit run dashboard/app.py

Opens at http://localhost:8501

Run Demo

python demo_script.py

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📚 Documentation

Project Structure

smart-energy-lader/
├── 📁 notebooks/                    # Jupyter notebooks for pipeline
│   ├── 01_data_simulation.ipynb    # Synthetic data generation
│   ├── 02_rl_training.ipynb        # Model training
│   ├── 03_model_validation.ipynb   # Model evaluation
│   └── 04_performance_analysis.ipynb # Analytics & reporting
├── 📁 dashboard/                    # Streamlit web application
│   ├── app.py                      # Main dashboard
│   ├── components/                 # Reusable UI components
│   └── requirements.txt            # Dashboard dependencies
├── 📁 models/                       # RL models & architecture
│   ├── trained_rl_model.pkl        # Trained PPO model
│   └── model_architecture.py       # Model utilities
├── 📁 factory_io/                   # Factory I/O integration
│   ├── factory_io_utils.py         # Scene & Modbus utilities
│   ├── scene_files/                # Factory scene configurations
│   └── modbus_config/              # Modbus register mappings
├── 📁 labview/                      # LabVIEW integration
│   ├── labview_integration.py      # Data exchange helpers
│   └── ai_integration_examples/    # Integration examples
├── 📁 docs/                         # Documentation
│   ├── architecture.md             # System architecture
│   └── setup_guide.md              # Setup instructions
├── 📁 tests/                        # Unit tests
├── demo_script.py                   # Complete demo
├── requirements.txt                 # Main dependencies
└── README.md                        # This file

Documentation Files

• Architecture Guide - System design and components
• Setup Guide - Installation and configuration
• API Reference - Detailed code documentation

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💻 Usage

1. Generate Training Data

jupyter notebook notebooks/01_data_simulation.ipynb

Generates synthetic Factory I/O operational data based on machine profiles.

2. Train RL Model

jupyter notebook notebooks/02_rl_training.ipynb

Trains PPO agent on simulated data (100,000 timesteps).

3. Validate Model

jupyter notebook notebooks/03_model_validation.ipynb

Evaluates trained model performance and generates metrics.

4. Analyze Results

jupyter notebook notebooks/04_performance_analysis.ipynb

Comprehensive statistical analysis and visualizations.

5. Monitor Dashboard

streamlit run dashboard/app.py

Real-time monitoring with AI control panel.

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🔌 Integration

Factory I/O Connection

from factory_io.factory_io_utils import parse_modbus_config, ModbusSimulator

config = parse_modbus_config('factory_io/modbus_config/config.json')
modbus = ModbusSimulator(slave_id=1)

LabVIEW Export

from labview.labview_integration import export_for_labview

export_for_labview(data, 'output/labview_data.json', format='json')

Custom Environments

Modify FactoryEnergyEnv in notebooks/02_rl_training.ipynb:

class FactoryEnergyEnv(gym.Env):
    def __init__(self, machine_config):
        self.action_space = spaces.Discrete(8)
        self.observation_space = spaces.Box(...)

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📊 Performance Metrics

Baseline vs Optimized (1-hour simulation)

Metric	Baseline	Optimized	Improvement
Total Energy	45.2 kWh	37.1 kWh	18.0%
Mean Power	45.2 kW	37.1 kW	18.0%
Peak Power	72.3 kW	39.8 kW	45.0%
Efficiency	85%	92%	7.0%

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🧪 Testing

Run Tests

pytest tests/ -v --cov=. --cov-report=html

Run Linting

flake8 . --max-line-length=120

Run Type Checking

mypy . --ignore-missing-imports

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🔧 Configuration

Adjust Factory Specifications

Edit factory_specs in training notebook:

factory_specs = {
    'conveyors': {'count': 14, 'max_power': 28},
    'pushers': {'count': 4, 'max_power': 12},
    'pick_place': {'count': 3, 'max_power': 12}
}

Tune RL Hyperparameters

Modify in 02_rl_training.ipynb:

model = PPO(
    "MlpPolicy",
    vec_env,
    learning_rate=3e-4,
    batch_size=64,
    n_steps=2048,
    gamma=0.99
)

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🐛 Troubleshooting

Issue	Solution
Missing dependencies	pip install --upgrade -r requirements.txt
Jupyter not starting	pip install --upgrade jupyter
Streamlit port in use	streamlit run app.py --server.port 8502
Model training slow	Enable GPU: pip install torch --index-url https://download.pytorch.org/whl/cu118

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🚀 Future Enhancements

• Multi-agent RL for distributed control
• Real-time model retraining pipeline
• 3D factory visualization
• Predictive maintenance integration
• Time-of-use electricity rate optimization
• Advanced SCADA connectivity
• Cloud deployment support

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📝 License

MIT License - see LICENSE for details

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🤝 Contributing

Contributions welcome! Please:

1. Fork repository
2. Create feature branch (git checkout -b feature/improvement)
3. Commit changes (git commit -am 'Add improvement')
4. Push to branch (git push origin feature/improvement)
5. Open Pull Request

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📞 Support

• Issues: GitHub Issues
• Documentation: See docs/ folder
• Examples: Check notebooks/ folder

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🙏 Acknowledgments

• Stable-Baselines3 team for RL library
• Factory I/O for industrial simulation
• Streamlit for dashboard framework
• Our research team and contributors

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Made with ⚡ by the Smart Energy Lader Team

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