Team D5 : SmartWatt

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
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• 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
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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.

Work Accomplished:

• Did some testing of a LSTM neural network architecture for time-series energy consumption prediction
• Tracked self-consumption metrics for solar utilization, peak reduction and cost savings % according to a baseline.
• The original cost is based on the predicted load with original device schedule. The optimized cost uses the new schedules with shifted loads

Mock backend for schedules : (I need to parse the results of optimization algo and figure out whats a good way to put that into device schedules). This is what the frontend should look like

Current display of predicted vs optimzied power. Need to scale this up accordingly once the power sensors are connected to Home Assistant and download the data from there.

Progress: I would say I am a little behind because a) the power sensor data is not logged in, need to do the integration of the backend with Home Assistant to feed that data, look at transience to analyze average power consumption b) The grid pricing data is fed in through Nordpool which has an integration  with Home Assistant, but for now I am training the LSTM by downloading a csv from Home Assistant rather than having it dynamically via an API (API calls are expensive)

Challenges and Next Steps:

• Improve load shifting algorithm with better device priority handling [figure out a way to parse info about optimized loads from an aggregate to a device level]
• Also right now, all devices have the same priority.  Need to assign weights based on device priorities (like running a fridge vs fan, fridge is way more important)

• Use these weights in the objective function to favor high-priority loads when minimizing cost or peak demand.

• Add detailed logging for performance metrics to validate optimization results

• Integration between HA and backend would be just creating a docker container and adding that to HA vs API requests

Work Accomplished:

Spent a significant amount of time debugging the optimization constraints and attempting different techniques to resolve DCP rule violations in the optimization model. To resolve it, I made sure the SOC dynamics equation is now linear in the decision variables, added fallback options (within CVXPy library) to try different solvers if the primary ECOS solver fails.

The SGD and L-BFGS-B methods are somewhat working, for the linear programming solvers its thinking the constraints are of conic form so I need to debug that.

Here is the output for SGD

Started working on a recommendations analyzer that processes energy data to identify patterns and anomalies. I developed a classification system that categorizes insights by priority and potential impact. Added functionality to track user actions on recommendations to improve future suggestions

Worked on the recommendations frontend.

For the frontend, I implemented a responsive dashboard that visualizes energy usage, production, and optimization results across devices

Challenges

One of the main challenges in SGD and LGBFS optimization algorithms is that the forecasted energy demand is very close to the actual load.

• If the forecasted load closely matches the actual load, then the gradient of the cost function is near-zero.
• This results in very small updates to optimization variables, reducing the ability of the algorithm to adapt or improve scheduling decisions.
• Mitigation Technique

Progress

I am on track regards to the schedule. Still waiting on the hardware integration to be complete and polling data from the power sensors, which will be fed into a database and processed to be displayed on the frontend.

Next Steps

• Power sensor data arrives at high frequency (every few seconds or milliseconds). A proper database in SQLite is required to be created for  logging and retrieval.
• When calculating average power consumption, factor in transients when devices switch on/off

• Develop interactive dashboards that display key metrics from the optimization algorithm, such as grid usage, battery state of charge, device schedules, and cost calculations.

• Start working on the integration of the backend with Home Assistant and ESPHome firmware. I have created the clients and API requests (communication protocol library) but actually need to start debugging