The understanding of the nature and structure of energy use in large buildings is vital for defining novel energy and climate change strategies. The advances on metering technology and low-cost devices make it possible to form a submetering network, which measures the main supply and other intermediate points providing information of the behavior of different areas. However, an analysis by means of classical techniques can lead to wrong conclusions if the load is not balanced. This paper proposes the use of a deep convolutional autoencoder to reconstruct the whole consumption measured by the submeters using the learnt features in order to analyze the behavior of different building areas. The display of weights and information of the latent space provided by the autoencoder allows us to obtain precise details of the influence of each area in the whole building consumption and its dependence on external factors such as temperature. A submetering network is deployed in the León University Hospital building in order to test the proposed methodology. The results show different correlations between environmental variables and building areas and indicate that areas can be grouped depending on their function in the building performance. Furthermore, this approach is able to provide discernible results in the presence of large differences with respect to the consumption ranges of the different areas, unlike conventional approaches where the influence of smaller areas is usually hidden.

中文翻译：

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使用深度学习从建筑计量表网络中提取特征。

对大型建筑物能源使用的性质和结构的理解对于定义新颖的能源和气候变化策略至关重要。计量技术和低成本设备的进步使形成子计量网络成为可能，该子计量网络可测量主电源和其他中间点，从而提供不同区域的行为信息。但是，如果负载不平衡，则通过经典技术进行的分析可能会得出错误的结论。本文提出使用深度卷积自动编码器，利用学习到的特征，重构由亚米表测量的总消耗量，以分析不同建筑区域的行为。通过自动编码器提供的权重和潜在空间信息的显示，我们可以准确了解每个区域对整个建筑物消耗的影响及其对外部因素（例如温度）的依赖性的精确细节。为了测试所提出的方法，在莱昂大学医院大楼中部署了一个计量表网络。结果表明，环境变量与建筑面积之间存在不同的相关性，并表明可以根据区域在建筑性能中的作用将其分组。此外，与通常隐藏较小区域的影响的常规方法不同，该方法在存在关于不同区域的消耗范围的较大差异的情况下能够提供可辨别的结果。