Global evaluation of WBGT and SET indices for outdoor environments using thermal imaging and artificial neural networks. (September 2020)

• Record Type: Journal Article
• Authors: Mahgoub, Ahmed Osama; Gowid, Samer; Ghani, Saud
• Is Part Of: Sustainable cities and society. Volume 60(2020)
• Publication Date: 2020-09
• Subjects: Heat stress -- Thermal comfort -- WBGT -- SET -- Outdoor environments -- Artificial neural networks; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102182
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: 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. Abstract: 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.