Work Accomplished
- Implemented conversational AI interface allowing users to query the system about energy optimization at their home.
- Created suggestion system that guides users with prompt examples for better engagement with the AI assistant
- The optimization system was enhanced with a user preferences framework that allows the user to select custom timeslots in which to run devices.
- The system incorporates earliest start times and latest end times for each scheduled device.
- Then designed a new constraint-based optimization that respects user preferences while maximizing energy savings
- A three-tier priority system was implemented:
- Low Priority: Maximizes energy savings with flexible timing (priority weight: 0.3)
- Medium Priority: Balances energy savings with user-preferred times (priority weight: 0.5)
- High Priority: Strictly adheres to user time preferences (priority weight: 0.8)
- Priority settings directly influence how the optimization algorithm weighs time constraints against cost savings
Progress
Frontend and backend up and ready.
Right now, I need to implement the API routes that will actually trigger device control — switching devices on or off based on user input or automated schedules. Slightly behind schedule with regards to the GANT chart.
These routes will act as the bridge between the UI actions and the actuation layer. Once the endpoints are set up and mapped to the appropriate device control logic, the system will be able to execute real actions, completing the loop from user interaction to physical outcome.
Next Steps
- Tie each route to the code that interacts with the device (ESP 32GPIO pins/ Home Assistant API).
- Then test with real devices to validate actual switching.
- Refine ML or linear programming algorithms that decide when to turn devices on/off.
- Incorporate feedback loops from usage data.