Global natural resources are affected by several causes such as climate change effects or unsustainable management strategies. Indeed, the use of water has been intensified in urban buildings because of the proliferation of HVAC (Heating, Ventilating and Air Conditioning) systems, for instance cooling towers, where an abundant amount of water is lost during the evaporation process. The measurement of the evaporation is challenging, so a virtual sensor could be used to tackle it, allowing to monitor and manage the water consumption in different scenarios and helping to plan efficient operation strategies which reduce the use of fresh water. In this paper, a deep generative approach is proposed for developing a virtual sensor for probabilistic estimation of the evaporation in cooling towers, given the surrounding conditions. It is based on a conditioned generative adversarial network (cGAN), whose generator includes a recurrent layer (GRU) that models the temporal information by learning from previous states and a densely connected layer that models the fluctuations of the conditions. The proposed deep generative approach is not only able to yield the estimated evaporation value but it also produces a whole probability distribution, considering any operating scenario, so it is possible to know the confidence interval in which the estimation is likely found. This deep generative approach is assessed and compared with other probabilistic state-of-the-art methods according to several metrics (CRPS, MAPE and RMSE) and using real data from a cooling tower located at a hospital building. The results obtained show that, to the best of our knowledge, our proposal is a noteworthy method to develop a virtual sensor, taking as input the current and last samples, since it provides an accurate estimation of the evaporation with wide enough confidence intervals, contemplating potential fluctuations of the conditions.

中文翻译：

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虚拟传感器，用于概率估算冷却塔中的蒸发

全球自然资源受到多种原因的影响，例如气候变化影响或不可持续的管理策略。实际上，由于HVAC（供暖，通风和空调）系统（例如冷却塔）的激增，城市建筑物中的水使用量已经增加，在蒸发过程中大量的水流失了。蒸发的测量具有挑战性，因此可以使用虚拟传感器来解决该问题，从而可以监视和管理不同情况下的耗水量，并帮助制定有效的操作策略以减少淡水的使用。在本文中，提出了一种深度生成方法，用于在给定周围条件的情况下开发用于虚拟估计冷却塔中蒸发量的虚拟传感器。它基于条件生成对抗网络（cGAN），条件生成对抗网络的生成器包括一个循环层（GRU），该层通过从先前状态中学习来对时间信息进行建模，而一个紧密连接的层则对条件的波动进行了建模。考虑到任何操作情况，提出的深度生成方法不仅能够得出估计的蒸发值，而且还可以产生整个概率分布，因此可以知道可能在其中找到估计值的置信区间。根据几种指标（CRPS，MAPE和RMSE）并使用来自医院大楼冷却塔的真实数据，对这种深层的生成方法进行了评估，并与其他概率方法进行了比较。所获得的结果表明，就我们所知，