sgcc_electricity_new helps users integrate their State Grid electricity bill and usage data into Home Assistant. The primary goal is to provide real-time monitoring of home electricity consumption, enabling users to track power usage and costs. The project solves the problem of manually tracking electricity bills and provides automation possibilities within Home Assistant. It uses Python and Selenium to navigate the State Grid website and extract the necessary data.
This project utilizes an offline neural network solution for captcha recognition, replacing reliance on commercial APIs. It offers a clear and configurable deployment process with Docker, simplifying the setup. The project supports both SQLite and MySQL databases for data storage. User-friendly configuration via a .env file allows for customization of parameters such as login credentials, time intervals, and notification settings.
- Entity Display: Provides Home Assistant entities for real-time voltage usage, charge balance, and historical usage data.
- Database Storage: Option to store daily electricity usage data in a SQLite database for historical analysis.
- Configuration Options: Extensive
.envfile for configuring login details, time intervals, notification settings, and device-specific parameters. - Docker Deployment: Simplified deployment using Docker with a Docker Compose file enabling easy setup and management.
- User Experience: Clear logs and informative messages assist in troubleshooting and monitoring the application's operation.
The project is actively maintained, with recent commits indicating ongoing development and bug fixes. It includes detailed documentation and a community discussion channel for support. The codebase is well-structured and modular, allowing for future enhancements and extensibility. The use of offline captcha solving contributes to the long-term reliability of the project.
This project benefits Home Assistant users who want to monitor their electricity consumption and costs. It provides valuable insights into energy usage, enabling users to make informed decisions about their energy consumption. The project offers a convenient alternative to manual tracking and provides a foundation for further automation workflows.