[Code can be viewed at : https://github.com/erika-24/smart-watt]
- Defined system parameters for power optimization (including the decision variables, established constraints for battery and load management, and objective function which is a function of grid power and solar energy produced)
- Started working on an optimization class that balances energy production, consumption, and storage.
- Utilized linear programming to optimize deferrable loads.
- Considered cost function variations: profit, cost minimization, and self-consumption.
- Used PuLP LP solver to solve the problem.
- Ran preliminary simulations on sample energy data
Finalized System Components
- Selected SQLite as the database for storing load consumption and power data, as well as outputs from the ML model
- Chose appropriate power sensors (INA226) and relay switches, as well as load profiles for the appliances for our model house
- Defined the embedded layer for system control (including all interactions between RPi5, Django backend, ML model, circuit components)
Completed the block diagrams for the design presentation and went over it as I will be presenting this week
Schedule & Progress
Waiting on Maya to send me real-time power consumption data once the power sensors arrive, so while the load consumption forecast model is almost done, it needs to be trained on real time data. I would say I am on schedule.
Next Steps
- Implement improvements to the LP solver configuration (including implementing the self consumption objective function)
- Begin integration with IoT power monitoring system (research how HTTP requests between ESP32 and the backend occur)
- Conduct further simulations to validate the algorithm’s robustness (on more real-time load consumption data)
- Start working on generating a data frame for grid pricing (via API calls)