Dynameletric is an advanced power monitoring and management system designed to optimize energy usage in electronic devices. By integrating IoT, AI, and machine learning technologies, this system provides real-time insights into power consumption, predicts usage trends, and minimizes energy waste. Dynameletric employs intelligent power management by embedding AI-driven models directly into hardware, fostering more sustainable and eco-friendly electronics while helping reduce the overall carbon footprint.
- Real-Time Data Collection: Continuously monitors power, current, and temperature metrics.
- AI-Driven Power Optimization: Predicts power usage patterns and optimizes energy consumption.
- Sustainable Hardware Design: Eco-friendly prototype developed with minimal energy waste.
- Data Analytics: Embedded data analysis to adjust operations dynamically for optimal power management.
Dynamelectric/
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│ ├── data.py # Data handling and preprocessing
│ ├── data2.py # Additional data processing code
│ ├── Finalcode.c # Final embedded code implementation
│ ├── hvac-architecture.mermaid # Architecture of HVAC system in mermaid diagram format
│ ├── meghacloudidecode.c # Cloud IDE code for integration
│ ├── modbuscode.c # Modbus protocol code for hardware communication
│ ├── model.py # Machine learning model implementation
│ ├── mselect.py # Model selection for best performance
│ ├── output.png # Sample output graph/visualization
│ ├── pzem code/ # Contains PZEM sensor-related code
│ ├── PZEM-004TCODE.c # Code for PZEM-004T sensor for power measurements
│ ├── rmse.py # RMSE calculation for model accuracy
│ ├── sensor_data.csv # Sample sensor data for testing and validation
│ └── Temperaturewithlcddisplaynomodbus.c # Temperature display code
- Python 3.8+: Required for running Python scripts.
- Embedded C Compiler: For compiling embedded C files.
- Mermaid: For visualizing system architecture diagrams (e.g., via VSCode extension).
- Modbus Libraries: Required for communication with Modbus-enabled devices.
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Clone the Repository
git clone https://github.com/your-username/Dynameletric.git cd Dynameletric -
Set up Virtual Environment
python3 -m venv env source env/bin/activate # On Windows use `env\Scripts\activate`
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Install Dependencies Install the Python dependencies using:
pip install -r requirements.txt
- Modbus Setup: Ensure Modbus-compatible devices are connected and configured as specified in
modbuscode.c. - PZEM Sensor: Connect the PZEM-004T sensors according to the instructions in
PZEM-004TCODE.c. - HVAC Configuration: Refer to
hvac-architecture.mermaidfor a visual overview of the HVAC system.
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Running the Model Execute
model.pyto initiate the AI-driven power optimization model.python model.py
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Data Processing Use
data.pyanddata2.pyfor data preprocessing steps, including sensor data transformation. -
Final Implementation Compile and run
Finalcode.con the embedded hardware to deploy the optimized power management solution.
The core embedded code responsible for real-time monitoring and control. It integrates data collection from various sensors and adjusts power output accordingly.
Implements the machine learning model for predicting power consumption patterns. The model is trained using historical data and evaluates performance using rmse.py.
This file contains the architecture diagram for an HVAC system. It provides a comprehensive view of the components involved in power management.
The file sensor_data.csv contains sample data collected from sensors. It includes metrics for temperature, power, and current, essential for testing and evaluating the AI model.
Sample results are visualized in output.png, demonstrating the model's performance in power optimization.
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch). - Commit your changes (
git commit -m 'Add feature'). - Push to the branch (
git push origin feature-branch). - Open a Pull Request.
This project is licensed under the MIT License. See the LICENSE file for details.
This project is maintained by the following individuals:
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Jsh Agarwal
Email: jshagarwal15@gmail.com -
Vipul Mhatre
Email: mhatrevipul1518@gmail.com -
Divy Mav
Email: divymav5@gmail.com -
Ankush Pandey
Email: ankushpandey436@gmail.com -
Ronit Mehta
Email: ronit.co80@tpoly.in