Abstract
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 the water-based cooling systems such as cooling towers, where a large amount of water is evaporated. In this paper, a method 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 León. Results show the probability distribution within which the estimation of evaporated water can be found, given the environmental and operating conditions.
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Alonso, S., Morán, A., Pérez, D., Prada, M.A., Fuertes, J.J., Domínguez, M. (2020). Probabilistic Estimation of Evaporated Water in Cooling Towers Using a Generative Adversarial Network. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_11
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