A Staged Selection Framework for Hybrid Natural Ventilation–HVAC Control Strategies in Tropical Mixed-Use Mid-Rise Buildings

Hybrid natural ventilation–HVAC systems offer significant potential for reducing building energy consumption while maintaining acceptable indoor thermal comfort, particularly in tropical climates. However, selecting appropriate control strategies remains challenging, as existing studies often prioritize theoretical performance over implementation and operational feasibility, especially in tropical mixed-use mid-rise buildings. This study proposes a staged selection framework for hybrid natural ventilation–HVAC control strategies, structured across the design, system integration, and operational phases of a building’s lifecycle. Informed by a structured synthesis of existing literature, the framework evaluates commonly reported control approaches, including Rule-Based Control, Model Predictive Control, Reinforcement Learning, and computational intelligence techniques. The framework is applied to a tropical mixed-use mid-rise building to demonstrate its practical relevance. Findings indicate that while advanced control strategies exhibit higher optimization potential, Rule-Based Control consistently emerges as the most viable solution under real-world constraints related to system complexity, data availability, and facility management capacity. The proposed framework provides a practical decision-support tool for context-aware control strategy selection in tropical urban environments.