Bytes to Watts Webinar

Welcome to the Bytes to Watts Webinar. This repository contains all the materials related to the webinar, including slides, scripts, resources, images, notes, and Q&A.

Contents

• slides/: Presentation slides
• scripts/: Example code snippets or demos
  • data/: Sample or real-time data files
    • solar_generation.csv: Simulated solar power generation data
    • energy_consumption.csv: Simulated load consumption data
    • battery_status.csv: Simulated battery charge/discharge levels
    • forecast_model.pkl: Saved ML model for predictions (optional)
  • monitoring/: Data visualization and real-time monitoring scripts
    • visualize_dashboard.py: Displays live solar, battery, and load data
    • stream_data.py: Simulates real-time data streaming
  • control/: Scripts for system control logic
    • energy_control.py: Basic rule-based control system
    • manual_control.py: Interactive CLI/web-based toggle for energy devices
  • optimization/: Energy optimization algorithms
    • optimize_usage.py: Load prioritization & battery management algorithm
  • prediction/: Forecasting energy generation and consumption
    • train_model.py: ML model training script
    • predict_energy.py: Predicts future energy usage and solar generation
  • utils/: Helper functions for data processing
    • data_loader.py: Reads and preprocesses CSV or API data
    • config.py: Stores system settings and constants
  • pyproject.toml: Dependencies and project configuration
  • README.md: Explanation of scripts and how to run the demo
• resources/: Related articles, papers, and documentation
• images/: Diagrams, charts, or visuals used in the presentation
• Q&A/: Collected questions and answers from the webinar

Getting Started

To get started with the demo project, follow these steps:

1. Clone the repository:

   git clone https://github.com/tinegachris/bytes-to-watts-webinar.git
   cd bytes-to-watts-webinar/scripts

2. Install the required dependencies using Poetry:

   pip install poetry
   poetry install

3. Run the real-time dashboard:

   streamlit run monitoring/visualize_dashboard.py

Project Overview

Smart Solar Monitoring

The smart solar monitoring project demonstrates how to optimize solar energy usage using simulated data. It includes the following components:

• Data: Simulated historical and real-time solar energy data.
• Dashboard: A Streamlit-based real-time dashboard to visualize energy data.
• Optimization: Scripts to optimize energy usage.
• Prediction: Modules to predict future energy generation and consumption.

Utility Scripts

The utility scripts include helper functions for data manipulation and visualization, making it easier to preprocess data and create charts or graphs.

Resources

Check the resources/ directory for related articles, papers, and documentation that provide additional context and information about the topics covered in the webinar.

Contributing

We welcome contributions to improve the project. Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes and commit them with clear messages.
4. Push your changes to your fork.
5. Open a pull request to the main repository.

Contact

For any questions or feedback, please open an issue in this repository.

Thank you for participating in the Bytes to Watts Webinar!