Global evaluation of WBGT and SET indices for outdoor environments using thermal imaging and artificial neural networks

•A novel method relies on thermal images for global evaluation of WBGT and SET.•Validation yielded a maximum average error of 11.4% for the WBGT global measurement.•Validation yielded a maximum average error of 8.5% for the SET global measurement.•An artificial neural networks (ANN) algorithm was used for further error reduction.•The method is applicable to all environments when varying correlation coefficients. The health and well-being of occupants of outdoor environments are largely affected by thermal stress, and therefore a global assessment is essential. Wet-bulb globe-temperature (WBGT) is used as a heat stress indicator and standard effective temperature (SET) is used as a thermal comfort index for assessment of thermal comfort in indoor and outdoor environments. These indices are usually evaluated point-wise which could be sufficient for relatively small spaces, but not suitable for large outdoor environments. This research proposes using a system combining climate sensors readings and thermal imaging to globally evaluate WBGT and SET values for outdoor environments. The algorithm derives air temperature from surface temperature values obtained using a thermal imaging camera. The obtained results were validated using readings of available sensors. Point-wise validation showed that the proposed methodology yielded results with a maximum average error of 11.4% compared to the average of point-wise local measurement for the WBGT, and an error of 8.5% for the SET. To minimize the error, an error reduction model based on artificial neural networks has been implemented. The error was further reduced to a maximum average error of 1.76% and 1.25% for WBGT and SET respectively.