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Open AccessArticle

Feature Extraction from Building Submetering Networks Using Deep Learning

Grupo de Investigación en Supervisión, Control y Automatización de Procesos Industriales (SUPPRESS), Esc. de Ing. Industrial, Informática y Aeroespacial, Universidad de León, Campus de Vegazana s/n, 24007 León, Spain
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3665;
Received: 9 May 2020 / Revised: 25 June 2020 / Accepted: 26 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Sensors for Medical and Industrial Applications)
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. View Full-Text
Keywords: deep learning; convolutional neural networks; autoencoders; submetering networks; power consumption deep learning; convolutional neural networks; autoencoders; submetering networks; power consumption
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MDPI and ACS Style

Morán, A.; Alonso, S.; Pérez, D.; Prada, M.A.; Fuertes, J.J.; Domínguez, M. Feature Extraction from Building Submetering Networks Using Deep Learning. Sensors 2020, 20, 3665.

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