Using data analysis scripts for home energy optimization

Community
1

Built a data processing pipeline for home energy optimization in Phoenix Arizona where summers exceed 115F and efficient HVAC management saves 15-18% on electricity bills.

The pipeline:

- Reads temperature sensor data every 30 seconds from 6 sensors

- Calculates rolling averages and detects anomalies

- Generates pre-cooling schedules based on weather forecast API

- Outputs control signals to the thermostat via MQTT

Key challenge was processing real-time sensor streams efficiently on a low-power Intel N100 mini PC. Memory-efficient data structures matter when you have 6 sensors × 2 readings/minute × 24 hours of rolling data.

The anomaly detection caught a failing HVAC capacitor early. Energy draw was 15% higher than the learned baseline for 3 consecutive days.

More context — saw a similar setup described in another community:

https://discuss.python.org/t/home-automation-and-energy-monitoring-for-extreme-desert-climate/107677

Anyone else building home automation data pipelines?

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Crystal is used in production by companies such as PlaceOS.
However, the linked Python forum page no longer seems to exist, and I could not tell from the post where Crystal was actually used. A little more detail on that point would be helpful.