In most residential buildings, a single thermostat commonly controls the HVAC system across multiple rooms, often overlooking spatial temperature differences and varying occupancy patterns. This study evaluates the limitations of such centralized control and explores alternative adaptive strategies in a residential-like laboratory setting. Experimental temperature data were collected from three distinct zones in the HIBO lab at the University of Nebraska–Lincoln, all operating under a shared setpoint. A sensor sensitivity analysis was conducted to assess the impact of sensor placement and quantity on measurement accuracy and control responsiveness. Three control strategies were implemented and evaluated: a baseline with a fixed setpoint, a shared occupancy-based control using a unified schedule, and an individual occupancy-based control applying zone-specific occupancy profiles. A comparative analysis was performed using a generalized HVAC load percentage to assess system activity during the test period. Results highlight the benefits of zone-level sensing and control in reducing HVAC activity and improving operational efficiency, offering insights for future residential HVAC control strategies.

Residential buildings are emerging as candidate spaces for the adoption of smart technologies, yet their specific needs and constraints remain underexplored compared to commercial settings. This review critically examines technologies that have already been successfully implemented or show strong potential for integration into residential environments. It highlights control strategies such as rule-based control, model predictive control, and reinforcement learning and their interplay with cutting-edge tools like the Internet of Things, smart sensors and virtual sensing in home environments. This paper uniquely focuses on the challenges and opportunities specific to residential applications, providing actionable insights into overcoming these barriers, and unlike prior work that emphasizes broader sustainability goals, it prioritizes the practical implications and feasibility of deploying these technologies in homes, offering a structured framework for understanding how these innovations can meet the nuanced demands of such spaces. Our review shows sustained growth in this literature since 2016, a predominance of experimental studies complemented by simulation-based and hybrid approaches, and relatively little work leveraging open public datasets. Activity is geographically concentrated in the United States, with strong contributions from East Asia and Europe. Across the residential context, the most persistent challenges are affordability and scalability, privacy and interoperability, and the limited generalizability of case-study deployments in occupied homes. Looking forward, the most promising directions combine occupant-centric MPC/RL with IoT-enabled infrastructures, alongside standardized evaluation protocols, shared datasets, and clearer evidence on costs to support scalable adoption.