A neuro-human thermal model was optimized to increase the prediction accuracy of the physiological variables of a group of 15 healthy male students exposed to transient environmental conditions. The effect of both the passive and active systems parameters was studied using a sensitivity analysis, and the parameters that had the most influence on the neuro-human thermal model outputs were established. A genetic algorithm was then used to optimize the model in order to determine the parameters that corresponded to the studied population. The results showed that the optimization increased the precision of the neuro-human thermal model. The mean absolute error and the maximum error between the experimental data and the numerical results for mean skin temperature were 0.13°C and 0.56°C, respectively, and we obtained 0.03°C and 0.11°C, respectively, for rectal temperature. These results show that the neuro-human thermal model can be accurately adjusted for the rectal, mean and local skin temperatures of a targeted population by using a genetic algorithm to determine the values of the parameters that correspond to this population.
Toward better estimation of HVAC Loads: integrating a detailed human thermal model into building simulation
