Abstract
Climate change is one of the direct consequences of high-speed urbanization faced by the world. Urban courtyards, parks, and squares are susceptible to thermal stress, especially in extreme climate zones. Even though the city planning policies in association to climate change are well considered over the years, yet sustainable urban planning and design instruments for the mitigation of the negative consequences of burdening cities on urban microclimate remain largely unexplored. From the thermal comfort point of view, the effects of current urban built form configurations have led to thermal discomfort inside the buildings which in turn has created the need to use such conditioning systems to cater thermal comfort needs of built environment users. Despite the fact that artificial neural networks (ANN) have largely been employed in various fields over past few decades, a recent trend and increasing interest has been observed in application of ANN in this field. The work presents a short overview of the few studies available in prediction of thermal comfort in outdoor built environment, highlighting the future research avenues.
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Shah, R., Pandit, R.K., Gaur, M.K. (2022). Artificial Neural Networks as a Tool for Thermal Comfort Prediction in Built Environment. In: Dubey, H.M., Pandit, M., Srivastava, L., Panigrahi, B.K. (eds) Artificial Intelligence and Sustainable Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1220-6_14
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