Probabilistic Estimation of Evaporated Water in Cooling Towers Using a Generative Adversarial Network

Water is a critical resource for life on the earth but it is becoming increasingly scarce. Therefore, water use should be sustainable and properly managed. The problem of water scarcity is still more stressed in cities, where buildings consume more and more water, especially commercial and institutional ones. In those buildings, HVAC (Heating, Ventilating and Air Conditioning) systems make an intensive use of water, especially thewater-based cooling systems such as cooling towers, where a large amount ofwater is evaporated. In this paper, amethod is proposed in order to estimate the evaporated water in cooling towers, considering the variations of environmental and operating conditions. We propose the use of a generative model which is able to generalize the estimation of the evaporated water, even in situations not included in the training data. A generative adversarial network (GAN) is used for training a deep learning-based generative model. The proposed method is tested using real data from a cooling tower located at the Hospital of Leon. Results show the probability distributionwithinwhich the estimation of evaporatedwater can be found, given the environmental and operating conditions.