Smart HVAC Control Strategies for Optimizing Thermal Comfort and Energy Efficiency in Omani Residential Buildings Under Extreme Heat Conditions

Abstract

Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the applicability and performance of advanced control strategies for Omani residential buildings operating under extreme heat conditions, synthesizing evidence from international research, the Gulf Cooperation Council (GCC) region, and Oman. Based on a systematic review of peer-reviewed literature published between 2015 and 2025, this analysis examines Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), Fuzzy Logic Control, and Internet of Things-based integrated approaches. International studies demonstrate that MPC strategies achieve energy savings of 16–40% compared to conventional thermostatic control by utilizing dynamic building thermal models to optimize control sequences over finite prediction horizons. DRL-based controllers achieve energy reductions of 17–23% through adaptive learning of optimal policies without requiring explicit system models, offering adaptability to dynamic occupancy patterns. Real-world implementation case studies from Oman and the GCC region—including the GUtech EcoHaus net-zero energy building and national-scale retrofit programs—demonstrate realized energy savings ranging from 25–75%, with higher savings achieved through comprehensive interventions that combine advanced controls with high-performance building envelopes. These findings suggest that substantial potential for reducing residential energy consumption while maintaining occupant thermal comfort under Oman’s extreme climatic conditions is achieved through the integration of advanced HVAC control strategies with high-performance building envelopes. Future research may address the development of occupant-centric adaptive comfort models calibrated for extreme heat conditions and context-specific control strategies that account for regional occupancy patterns and cultural preferences.