AI Smart Thermostat based on ESP32-C5

Client

An energy efficiency startup creating a thermostat that requires zero manual programming while maximizing HVAC savings.

Challenge

Accurately detecting room occupancy patterns and thermal lag characteristics without cloud dependency, and predicting optimal setpoint transitions.

Product Concept & Architecture

The ESP32-C5 provides Matter-compatible Thread/Wi-Fi connectivity. A 60GHz mmWave radar module (LD2410) detects presence, motion, and micro-movements (breathing). An SHT45 sensor provides ±0.2°C temperature accuracy. A 1.54-inch E-Ink display shows current status.

Engineering Solution

We deployed ESP-IDF with a custom occupancy prediction algorithm. The system learns occupancy patterns over 7 days using a time-series clustering approach on-device. Thermal lag of the building is modeled through a simple RC-equivalent parameter estimator. When the user leaves, the thermostat enters eco mode but tracks the cool-down rate to pre-heat/cool before predicted return time. All data stays local.

Result

25% average HVAC energy reduction in field trials across 50 homes. 97% occupancy prediction accuracy after 14 days of learning. No cloud dependency for core features.