Determining the influence of meteorological parameters on outdoor thermal comfort using ANFIS and ANN

Abstract

The study aims to develop artificial neural networks for prediction of outdoor thermal comfort using meteorological parameters as input parameters. Universal Thermal Climate Index (UTCI) is used as the target parameter. For this purpose, a total number of 5088 hours of field monitoring data was considered from four representative urban streets of Gwalior city, India. First, linear association was determined between meteorological parameters. Mean radiant temperature was to be in high correlation with globe temperature and surface temperature. Second, Adaptive Neuro Fuzzy Inference System (ANFIS) was used to rank the meteorological parameters in order of their impact on UTCI. Air temperature was found to be having highest influence. Third, ANN models are developed to predict UTCI with air temperature as the only meteorological parameter in input layer. The developed ANN models for all four streets show remarkable predictive ability for both summer (R2 = 0.852, 0.986, 0.962, 0.955) and winter season (R2 = 0.976, 0.870, 0.941, 0.950). Additionally, the success index of the developed models is found to be in range 0.73 – 1, 0.88 – 1, 0.86 – 1, 0.87 – 1 for summer season and 0.78 – 0.99, 0.61 – 0.98, 0.55 – 0.98, 0.87 – 0.99 for winter season. The study contributes to the smart city initiatives for future urban designing by establishing that outdoor thermal comfort can be easily predicted using air temperature when other microclimatic parameters are difficult to record using machine learning approach.