Prediction of mean radiant temperature distribution around a building in hot summer days using optimized multilayer neural network model

Abstract

Urban warming has become increasingly serious in recent years. Especially in the case of hot summer days (with maximum daily temperature greater than 30°C), the number of people suffering from heat stroke increases every year. To mitigate urban warming and adapt to it, many researchers have focused on improving outdoor thermal comfort. The mean radiant temperature (MRT) is one of the most important variables affecting human thermal comfort in outdoor urban spaces; however, the process of MRT calculation requires a lot of computing power and time. The main objective of this study is the optimization of the back propagation and genetic algorithms on the basis of a multilayer neural network (MLNN), as an alternative to the time-consuming and computationally intensive process for quick MRT prediction. In this research work, a neural network was trained using the relevant weather-related and building morphology-related parameters that may affect the MRT from 2014-2018 as training parameters, the distributions of MRT around buildings in hot summer days of 2019 were predicted using an optimized neural network model. The results show that the mean absolute percentage error (MAPE) and root-mean-square error (RMSE) of the optimized model were lower than 1% and 1°C.