Cluster Computing

, Volume 22, Supplement 1, pp 1065–1077 | Cite as

Collaborative data analytics for smart buildings: opportunities and models

  • Sanja Lazarova-MolnarEmail author
  • Nader Mohamed


Smart buildings equipped with state-of-the-art sensors and meters are becoming more common. Large quantities of data are being collected by these devices. For a single building to benefit from its own collected data, it will need to wait for a long time to collect sufficient data to build accurate models to help improve the smart buildings systems. Therefore, multiple buildings need to cooperate to amplify the benefits from the collected data and speed up the model building processes. Apparently, this is not so trivial and there are associated challenges. In this paper, we study the importance of collaborative data analytics for smart buildings, its benefits, as well as presently possible models of carrying it out. Furthermore, we present a framework for collaborative fault detection and diagnosis as a case of collaborative data analytics for smart buildings. We also provide a preliminary analysis of the energy efficiency benefit of such collaborative framework for smart buildings. The result shows that significant energy savings can be achieved for smart buildings using collaborative data analytics.


Smart buildings Collaborative data analytics Models Energy efficiency Fault detection and diagnosis 



Funding was provided by Innovation Fund Denmark (Grant No. 4106-00003B).


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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.University of Southern DenmarkOdenseDenmark
  2. 2.Middleware Technologies LabPittsburghUSA

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