The present study, deals with the 24-hour prognosis of the outdoor biometeorological conditions in an urban monitoring site within the Greater Athens area, Greece. For this purpose, artificial neural networks (ANNs) modelling techniques are applied in order to predict the maximum and the minimum value of the physiologically equivalent temperature (PET) one day ahead as well as the persistence of the hours with extreme human biometeorological conditions. The findings of the analysis showed that extreme heat stress appears to be 10.0% of the examined hours within the warm period of the year, against extreme cold stress for 22.8% of the hours during the cold period of the year. Finally, human thermal comfort sensation accounts for 81.8% of the hours during the year. Concerning the PET prognosis, ANNs have a remarkable forecasting ability to predict the extreme daily PET values one day ahead, as well as the persistence of extreme conditions during the day, at a significant statistical level of . 1. Introduction The impact of climate and prevailing weather on human thermal comfort discomfort is almost obvious. Environmental conditions affect the heat balance between the human body and the environment and they are the source of possible discomfort conditions. In particular, during the summer period, extreme meteorological conditions have a direct impact on energy consumption of buildings for air-conditioning purposes [1]. It has been reported as an increase of about 800% in annual purchases of air-conditioning units ever since, due to the serious heat waves observed in Greece during 1987–1989 [2]. Human thermal comfort or discomfort conditions may be assessed through a large number of theoretical and empirical indices requiring usually a larger or smaller number of input microclimate parameters such as air temperature, wind speed, and air humidity [4–6]. An important issue, in terms of human health risk assessment, is to predict the microclimate and the associated human thermal comfort-discomfort conditions in the urban environment. Despite the existence of various microclimate models, there are only a few models that are able to deal with human thermal comfort estimations, for example, the RayMan model [7, 8] and the Envi-Met model [9]. These models may be used efficiently in both estimating and predicting human thermal comfort conditions in the urban environment [10–13]. The present study deals with the application of artificial neural networks (ANNs), an alternative modeling technique against common modeling efforts for the evaluation and the prognosis of
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