Sensitivity analysis of Gagge’s Standard Effective Temperature for fan-first cooling control strategy in tropical mixed-mode buildings

• MM strategy combines CF and AC in a Fan-First approach to enhance comfort and savings. • SET addresses tropical-condition limits and supports dynamic Fan-First control. • Sobol and Shapley's analysis shows air velocity and clothing contribute 47–66 % to SET. • Analysing comfort models requires a careful selection of input distribution. • Proposed methodology is reproducible to other climates and models. Mixed-mode (MM) cooling solutions integrate ceiling fans (CF) and traditional air conditioning (AC) simultaneously in a “ Fan-first ” scenario, enhancing comfort while reducing energy use. However, this dual approach complicates system sizing and consumption assessment due to the added layer of comfort control. To address this, we advocate using Gagge's Standard Effective Temperature (SET) for MM cooling control in the tropics. We also evaluate its sensitivity to air velocity, clothing, and temperature, emphasising its applicability for control algorithms in Building Energy Models (BEM). BEM cooling algorithm should leverage fans' air velocity and clothing flexibility as adaptive opportunities. This study first highlights the relevance of SET over alternative models found in the literature for tropical applications. Second, we use environmental datasets to assess air velocity and clothing contributions alongside temperature setpoints through two Sensitivity Analysis (SA) techniques. Traditional Sobol's indices, assuming input independence, confirm that temperature is not the sole parameter influencing SET. More advanced Shapley Effect techniques, accommodating representative input distributions, reveal that air velocity and clothing outweigh temperature within an airspeed range of [0–2 m.s -1 ]. This work provides new insights for designing actionable and adaptive control strategies for comfort in tropical indoor conditions to simulate and size MM-coupled cooling systems effectively. It also underscores the benefits of advanced SA techniques in evaluating comfort models.