TY - JOUR
T1 - Collaborative data analytics for smart buildings
T2 - opportunities and models
AU - Lazarova-Molnar, Sanja
AU - Mohamed, Nader
N1 - Funding Information:
Funding was provided by Innovation Fund Denmark (Grant No. 4106-00003B).
Publisher Copyright:
© 2017, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - 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.
AB - 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.
KW - Collaborative data analytics
KW - Energy efficiency
KW - Fault detection and diagnosis
KW - Models
KW - Smart buildings
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U2 - 10.1007/s10586-017-1362-x
DO - 10.1007/s10586-017-1362-x
M3 - Article
AN - SCOPUS:85034072729
SN - 1386-7857
VL - 22
SP - 1065
EP - 1077
JO - Cluster Computing
JF - Cluster Computing
ER -