| heal.abstract |
In this research, an artificial neural network model (ANN) was applied to estimate the thermal comfort conditions in the mountainous regions of Gerania (MG) and of Nafpaktia (MN) in Greece. Air temperature and relative humidity were recorded from June to August 2007 at two selected sites for each study region. Data of the aforementioned parameters were used for the calculation of the thermohygrometric index (THI), from which thermal comfort conditions were evaluated as classes. The ANN model, the multilayer perceptron (MLP) was used for the estimation of THI values at the examined high altitude level (1334 and 1338 m in MG and MN, respectively) based on the temperature and the relative humidity of the examined low altitude level (650 m in MG and 676 m in MN), taking into account the actual time of measurement (ATM). The results of the development and application of this extended MLP model indicated more accurate estimations of THI values at the two study regions during the whole day period compared to the MLP application without the use of ATM. Also, the extended model, examining the whole day, showed more accurate estimations of THI values in MG compared to MN. Similarly, this model provided better estimations separately for both daytime (09h00min-20h00min) and nighttime (21h00min-08h00min) in comparison with the respective THI estimations taking into account only the air temperature and relative humidity as input parameters. Additionally, the extended MLP model was more efficient estimating THI values during daytime hours compared to nighttime hours in both MG and MN. Also, the extended MLP model was more capable in estimating better the THI values in the ""hot"" class in MG as well as in the ""comfortable"" class in MN. |
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