Development of a predictive indoor clothing insulation model for Sydney

Clothing insulation is a key input to thermal comfort models such as the Predicted Mean Vote, yet office attire insulation values are typically based on rough seasonal estimates. This paper develops a locally relevant clothing database for Sydney office buildings and a predictive clothing insulation model based on outdoor temperature forecasts, using longitudinal data from an air-conditioned finance-sector office. Gender significantly influenced clothing choices (p < 0.001), though corporate dress codes and seasonal weather often override gendered thermal preferences. The analysis of 185 days of data showed that forecasted daily maximum outdoor temperature, combined with gender, dress code, and season, provided the highest explanatory power (adjusted R2 = 0.414), with observed daily average temperature as a viable alternative (adjusted R2 = 0.408). Cross-validation yielded a mean RMSE of 0.089 clo and MAE of 0.072 clo. The model offers a calibrated alternative to static ASHRAE 55 estimates for Sydney offices.