PREDICTION OF PSYCHROMETRIC PARAMETERS USING NEURAL NETWORKS

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Abstract
A neural network approach was used for the prediction of the psychrometric parameters in a non-iterative manner. Neural network models were developed for the each of the three main variables - dry bulb temperature, wet-bulb temperature and relative humidity - as a function of the other two variables. Models were also developed for the prediction of the dew point temperature using the dry bulb and wet bulb temperatures, and for the saturation vapor pressure as a function of the dry bulb temperature. The sensitivity of the neural network performance to the form of input and output variables employed was also investigated. The prediction accuracy of the neural network models were found to be very good, with errors less than 4%.

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