Optimization of Zonal Heating Using Adaptive Machine Learning Algorithms

This paper presents a comprehensive study on the optimization of zonal heating in residential environments using adaptive machine learning algorithms, based on the patented SmartThermMesh system. The system integrates distributed IoT-based controllers, a mesh communication network, and predictive control algorithms that autonomously regulate microclimates across multiple zones. Experimental and simulated results demonstrate a 32–38% reduction in overall HVAC energy consumption compared to conventional single-thermostat systems, with enhanced comfort stability and reduced equipment cycling. The research combines real-world performance data with model-based simulations calibrated according to DOE and ASHRAE residential heating standards. A comparative analysis versus state-of-the-art solutions confirms the superior performance of adaptive zonal optimization using reinforcement and predictive learning strategies.