Risks
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We are training LSTM/CNN models based off of data polled from sensors. Retraining the model every request (if the cache fails) can cause lag
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Home Assistant API latency spikes can delay forecasts. Plotting charts via
matplotlibadds overhead (200–300ms) - Caching logic failing silently if timestamps are misaligned
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Infeasible LP results due to overlapping constraints or underforecasted solar
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LP objective may optimize cost but ignore user experience (e.g., clustering devices)
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Forecast errors can propagate into LP, yielding poor schedules
Changes
The initial approach used an LSTM network to forecast solar generation based on time-series data from sensor.maya_solar_panel. Main challenge was high latency due to recurrent nature and sequential computation and overfitting on short term trends rather than daily cycles. Transitioned to a 1D Convolutional NN. CNNs capture short-term temporal correlations ( sunrise to peak curve) better.
Progress
- Successfully polled data from all sensors into backend
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CNN model produces realistic solar forecasts aligned with production hours
- Performed testing on API response times
Verification/Validation
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Used curl requests to profile API timing in the terminal while polling data from sensors
- Unit tested LP constraints to ensure feasibility and logic correctness
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Compared cached vs. uncached outputs for CNN model for correctness and stability
- Compared forecast vs. actual solar power to assess prediction alignment
- To validate the 15% energy savings, we’ll compare historical baseline energy usage (from fixed schedules) against SmartWatt’s optimized schedules using the same input conditions. Using sensor data (from
sensor.maya_fan_power,sensor.maya_solar_panel), we’ll simulate both scenarios over a 1–2 week period and calculate daily energy costs. The optimized case will use CNN-based solar forecasts and LP scheduling. If the average cost reduction exceeds 15% compared to the baseline, the savings target is met. - To verify the robust assembly of the house, I will carry the house around and apply reasonable force to ensure it will remain intact during the demo.
- To validate the integration of the hardware components with the house, I will perform a fit test with the interior components. I will ensure that all feedthrough holes are wide enough and that the components can be neatly and securely placed inside their respective rooms.
- For validation, we plan to conduct user testing sessions where participants interact with the model house and dashboard to simulate their household energy consumption. This will help us evaluate whether SmartWatt creates a coherent and immersive experience. We will analyze user feedback to determine if the system effectively communicates the intended purpose of the project, the scaled energy usage and passage of time, and feels intuitive to interact with.
