TY - GEN
T1 - Towards collaborative data analytics for smart buildings
AU - Lazarova-Molnar, Sanja
AU - Mohamed, Nader
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2017.
PY - 2017
Y1 - 2017
N2 - Smart buildings are buildings equipped with the latest technological and architectural solutions, controlled by Building Management Systems (BMS), operating in fulfillment of the typical goals of increasing occupants’ comfort and reducing buildings’ energy consumption. We witness a slow, but steadily increasing trend in the number of buildings that become smart. The increase in availability and the decrease in prices of sensors and meters, have made them almost standard elements in buildings; both in newly built and existing ones. Sensors and meters enable growing collections of data from buildings that is available for further analytics to support meeting BMS’ performance goals. For a single building to benefit from this data-based analytics, it will take a long time. Collaboration of BMS in their data analytics processes can significantly shorten this time period. This paper makes two contributions: one, a careful examination of the potential of buildings for collaborative data analytics; and two, description of models for collaborative data analytics.
AB - Smart buildings are buildings equipped with the latest technological and architectural solutions, controlled by Building Management Systems (BMS), operating in fulfillment of the typical goals of increasing occupants’ comfort and reducing buildings’ energy consumption. We witness a slow, but steadily increasing trend in the number of buildings that become smart. The increase in availability and the decrease in prices of sensors and meters, have made them almost standard elements in buildings; both in newly built and existing ones. Sensors and meters enable growing collections of data from buildings that is available for further analytics to support meeting BMS’ performance goals. For a single building to benefit from this data-based analytics, it will take a long time. Collaboration of BMS in their data analytics processes can significantly shorten this time period. This paper makes two contributions: one, a careful examination of the potential of buildings for collaborative data analytics; and two, description of models for collaborative data analytics.
UR - http://www.scopus.com/inward/record.url?scp=85016101094&partnerID=8YFLogxK
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U2 - 10.1007/978-981-10-4154-9_53
DO - 10.1007/978-981-10-4154-9_53
M3 - Conference contribution
AN - SCOPUS:85016101094
SN - 9789811041532
T3 - Lecture Notes in Electrical Engineering
SP - 459
EP - 466
BT - Information Science and Applications 2017 - ICISA 2017
A2 - Kim, Kuinam
A2 - Joukov, Nikolai
PB - Springer Verlag
T2 - 8th International Conference on Information Science and Applications, ICISA 2017
Y2 - 20 March 2017 through 23 March 2017
ER -