Risks:
Gradient Step Update Issue:
- In SGD and L-BFGS optimization algorithms, when forecasted energy demand closely matches actual load, the gradient of the cost function is near-zero.
- This results in very small updates to optimization variables, reducing adaptability and making it harder for the model to improve scheduling decisions.
- Mitigation: Introduce regularization techniques, perturbations in forecast data (white Gaussian noise)
Misinterpreting Transients as Long-Term Load Changes
Short-term power spikes (when a fan starts) can be mistaken for sustained high energy demand. The optimizer may overcompensate by reducing battery discharge or increasing grid imports unnecessarily.
Changes:
No major changes happened this week. We are thinking of adding a user tracking functionality to improve future recommendations based on past actions to make the system more interactive.
Progress:
- Optimization Model Debugging: Refined solver selection and fixed SOC linearity issues.
- Created CAD model of the model house
- Algorithm Development: Partial success with SGD & L-BFGS, but linear programming constraints need debugging.
- Recommendation Engine: Classifies insights by priority and impact, with user tracking integration.
- Frontend: Built an interactive energy visualization dashboard.
Pending: - Hardware Integration: Waiting for power sensor polling to complete before full backend integration.
- Building the model house in which to house all of our electrical components.