Collaborative data analytics for smart buildings: opportunities and models

Sanja Lazarova-Molnar, Nader Mohamed

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1065-1077
Number of pages13
JournalCluster Computing
Volume22
DOIs
Publication statusPublished - Jan 16 2019
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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