Abstract
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.
Highlights
In developed countries, power consumption from buildings has steadily increased in the last few decades and this growing trend will continue in the future
Non-Intrusive Load Monitoring (NILM) enables the analysis of the total power consumption through signal processing algorithms, which are applied to the collected data from a single point in order to achieve an improved demand-side energy usage [9]
A new methodology is presented in this paper to take advantage of a submetering architecture for a better understanding of the energy use patterns in large buildings
Summary
Power consumption from buildings has steadily increased in the last few decades and this growing trend will continue in the future. We propose the development of a submetering network in buildings with the aim of understanding the consumption patterns corresponding, to the main supply and to each area of the building. The meters comprising the submetering network are in charge of measuring and collecting data used to analyze the consumption profiles. Energy bills can be used as an information source in order to disaggregate the power consumption of different areas and analyze the energy use in buildings [4]. Non-Intrusive Load Monitoring (NILM) techniques [6] are able to disaggregate power consumption and discern devices or demands from the aggregated data acquired from the main energy meter [7]. NILM enables the analysis of the total power consumption through signal processing algorithms, which are applied to the collected data from a single point in order to achieve an improved demand-side energy usage [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
