Risks
During demo testing, router issues on Maya’s laptop temporarily disrupted Raspberry Pi connectivity. We are switching to a new device router for stability. This can be a network setup challenge since we havent fully tested communication over this new network.
Changes
We changed the setup to use a standalone router for the Pi. the Pi connects to Wi-Fi network using the router.
Progress
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Unit Testing
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Tested device control APIs
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Evaluated forecasting model accuracy (MAPE/RMSE) on solar and price data.
- User Testing & User satisfaction
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Frontend-Backend Integration Progress:
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Connected backend schedule outputs to frontend dashboard for live visualization.
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Unit Tests Conducted
- Device Control Test: Verified API calls successfully switched smart devices ON/OFF at the correct time by looking at system logs
- Optimization Solver Test: Validated LP outputs feasible schedules under varying constraints, edge cases include flat/spiky data for prices and solar output
- Forecasting Model Test: Evaluated regression predictions against known solar generation and pricing data.
- UI Functionality Test: Confirmed correct display of real-time power data, scheduled actions, and user overrides.
- Front-End API Connectivity Test: Confirmed that React frontend could successfully call FastAPI endpoints and receive responses.
Overall System Tests:
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End-to-End Scheduling Test: Simulated a full daily cycle from sensing → predicting → optimizing → scheduling → controlling devices.
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Stress Testing: Input flat load profiles and sudden random spikes to test optimization stability and system robustness.
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Latency and Responsiveness Test: Measured UI response time under background optimization tasks.
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End-to-End API Test (Chatbot): Tested communication between chatbot UI and backend, ensuring user input is captured and responses are sent back
Findings and Design Changes
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Edge Case Handling: Stress testing exposed situations when no feasible schedule existed (overlapping tight constraints); we added fallback rules.
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API Logging Addition: Initial debugging was slow without detailed API logs. We added full request/response logging to trace failures and latency bottlenecks.
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Shift to Asynchronous Processing: Originally, synchronous optimization caused UI freezes. Moving to background task execution using FastAPI improved responsiveness.
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Prediction Model Tuning: Retrained regressions models with better feature scaling and added time-of-day features to improve accuracy in volatile solar conditions.