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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
BIO Intelligence Service: Water performance of buildings. Tech. rep. Final report prepared for European Commission, DG Environment (2012)
Bowerman, B., O’Connell, R., Koehler, A.: Forecasting, Time Series, and Regression: An Applied Approach. Duxbury Advanced Series in Statistics and Decision Sciences. Thomson Brooks/Cole, Belmont (2005)
Dziegielewski, B., Kiefer, J.C., Opitz, E.M., Porter, G.A., Lantz, G.L.: Commercial and Institutional End Uses of Water. American Water Works Association Research Foundation (2000)
Eades, W.G.: Energy and water recovery using air-handling unit condensate from laboratory HVAC systems. Sustain. Cities Soc. 42, 162–175 (2018). https://doi.org/10.1016/j.scs.2018.07.006
Famiglietti, J.S.: The global groundwater crisis. Nat. Clim. Change 4(11), 945–948 (2014). https://doi.org/10.1038/nclimate2425
Gao, M., Sun, F.Z., Zhou, S.J., Shi, Y.T., Zhao, Y.B., Wang, N.H.: Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions. Int. J. Therm. Sci. 48(3), 583–589 (2009). https://doi.org/10.1016/j.ijthermalsci.2008.03.012
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates, Inc. (2014)
Gui, J., Sun, Z., Wen, Y., Tao, D., Ye, J.: A review on generative adversarial networks: Algorithms, theory, and applications. CoRR abs/2001.06937 (2020). https://arxiv.org/abs/2001.06937
Hawit, O., Jaffe, T.: Water-energy nexus: heat rejection systems. ASHRAE J. 59(9), 28–39 (2017)
Hosoz, M., Ertunc, H., Bulgurcu, H.: Performance prediction of a cooling tower using artificial neural network. Energy Convers. Manage. 48(4), 1349–1359 (2007). https://doi.org/10.1016/j.enconman.2006.06.024
Jin, G.Y., Cai, W.J., Lu, L., Lee, E.L., Chiang, A.: A simplified modeling of mechanical cooling tower for control and optimization of HVAC systems. Energy Convers. Manage. 48(2), 355–365 (2007). https://doi.org/10.1016/j.enconman.2006.07.010
Koochali, A., Schichtel, P., Ahmed, S., Dengel, A.: Probabilistic forecasting of sensory data with generative adversarial networks - ForGAN. CoRR abs/1903.12549 (2019). http://arxiv.org/abs/1903.12549
Koop, S.H.A., van Leeuwen, C.J.: The challenges of water, waste and climate change in cities. Environ. Dev. Sustain. 19, 385–418 (2017). https://doi.org/10.1007/s10668-016-9760-4
Kundzewicz, Z.W., Döll, P.: Will groundwater ease freshwater stress under climate change? Hydrol. Sci. J. 54(4), 665–675 (2009). https://doi.org/10.1623/hysj.54.4.665
Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. CoRR abs/1609.04802 (2016). http://arxiv.org/abs/1609.04802
Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR abs/1411.1784 (2014). http://arxiv.org/abs/1411.1784
Qureshi, B.A., Zubair, S.M.: A unified approach to predict evaporation losses in evaporative heat exchangers. Int. J. Refrig. 34(8), 1866–1876 (2011). https://doi.org/10.1016/j.ijrefrig.2011.06.008
Richey, A.S., Thomas, B.F., Lo, M.H., Reager, J.T., Famiglietti, J.S., Voss, K., Swenson, S., Rodell, M.: Quantifying renewable groundwater stress with GRACE. Water Resour. Res. 51(7), 5217–5238 (2015). https://doi.org/10.1002/2015WR017349
Stec, A.: Sustainable Water Management in Buildings, Water Science and Technology Library, vol. 90. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-35959-1
Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1283–1292, July 2017. https://doi.org/10.1109/CVPR.2017.141
Weimar, D., Browning, A.: Reducing water costs in building HVAC systems. Facilities Engineering Journal 37(3), 24–26 (2010)
Yu, Y.B., Canales, S.: Conditional LSTM-GAN for melody generation from lyrics. ArXiv abs/1908.05551 (2019)
Zhang, K., Zhong, G., Dong, J., Wang, S., Wang, Y.: Stock market prediction based on generative adversarial network. Procedia Comput. Sci. 147, 400–406 (2019). https://doi.org/10.1016/j.procs.2019.01.256
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-48791-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48790-4
Online ISBN: 978-3-030-48791-1
eBook Packages: Computer ScienceComputer Science (R0)