Edge AI Embedded Systems for Smart Buildings: TinyML IAQ Inference for HVAC Optimization
Introduction: The Challenge of Real-Time, Private IAQ Monitoring
Facilities teams required near-real-time indoor air quality (IAQ) insights but were constrained by continuous cloud streaming (privacy, bandwidth, cost). They needed a robust, low-cost edge solution to dynamically maintain IAQ thresholds and reduce HVAC energy consumption, necessitating an innovative smart infrastructure solution.
Solution Overview: TinyML and On-Device HVAC Control
EurthTech integrated TinyML models into a constrained MCU node (ESP32 - S3 family) to detect occupancy-related IAQ anomalies (high VOC, CO_2 rises) and trigger local HVAC actions. The system uses a multi-modal classifier, quantised to 8 - bit (TFLite Micro) for on-device inference in <50 ms. This successful on-device inference capability is a prime example of an Edge AI embedded system.
Technical Implementation: Embedded Systems Development and AI Engineering
The embedded systems development included an ultra-low-power co-processor to manage high-frequency sensor sampling and deep sleep. The AI engineering team trained a classifier on windowed spectral features and time-domain statistics. The device employed an adaptive thresholding system, uploading only summaries/alerts when confidence exceeded a threshold, which reduced data transmission by >90%. For real-time control, the node communicates with the local BMS via secure MQTT to trigger pre-emptive ventilation.
Results & Impact: AI Engineering and Operational ROI
In a 5 - zone pilot, the edge AI detected occupancy - linked VOC/PM events with 89% F1 - score, successfully reducing unnecessary HVAC runtime by ~16% while maintaining IAQ thresholds. The combination of TinyML and edge pre-filtering produced substantial operational savings: lower network/ingress costs, reduced cloud compute, and faster actuation—a high - ROI solution.
Scalability & GIS Context: We provided a provisioning flow that includes secure device identity, OTA for model updates, and a field calibration routine for sensors. The deployment of smart IAQ across a campus or building portfolio creates a localized Digital twin smart city model for energy management, perfectly aligning with the portfolio of a GIS consulting company and a smart city solutions provider.