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Lookup NU author(s): Dr Xiang XieORCiD, Dr Tejal Shah, Professor Mohamad Kassem, Professor Philip James
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been accepted and is due to be published in its final definitive form by LDAC, 2023.
For re-use rights please refer to the publisher's terms and conditions.
The storage and processing of massive time series data collected from smart buildings consume considerablecomputational resources. However, major information redundancy can be found in the smartbuilding data. This paper proposed a partial correlation graph based approach to map the dependenciesamong sensors and detect the sensor communities in which the sensors are strongly “net” correlated.Specifically, the sparse partial correlation estimation method is used to learn the partial correlation graph.The Louvain algorithm is used to detect the communities of sensors by optimising the graph modularity.The case study demonstrates that the proposed method can identify spare sensors in the detected sensorcommunities and thus enhance the computational feasibility of smart building applications.
Author(s): Xie X, Herrera M, Shah T, Kassem M, James P
Publication type: Conference Proceedings (inc. Abstract)
Publication status: In Press
Conference Name: 11th Linked Data in Architecture and Construction Workshop
Year of Conference: 2023
Acceptance date: 01/05/2023
Date deposited: 03/05/2023
Publisher: LDAC
URL: https://linkedbuildingdata.net/ldac2023/
