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
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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