Optimization and metamodelization based on machine learning of a new neuro human thermal model

Nowadays, due to climate change, heatwaves become stronger in terms of frequency and intensity. This phenomenon can have serious impact on the indoor environments, indoor thermal comfort and on public health. These situations of high indoor thermal conditions can expose the occupants to health risks such as hyperthermia, dehydration, and heat strokes. Then, the estimation of these risks is crucial. The currently used indices to estimate health risks such as WBGT, HSI and PHS are generally dedicated to outdoor environments and for subjects exerting heavy activities.