Knowledge Discovery Using Topological Analysis for Building Sensor Data

Journal article

Gupta, M. and Phillips, N. (2020). Knowledge Discovery Using Topological Analysis for Building Sensor Data. Sensors. 20 (17), p. e4914. https://doi.org/10.3390/s20174914
AuthorsGupta, M. and Phillips, N.
AbstractDistributed sensor networks are at the heart of smart buildings, providing greater detail and valuable insights into their energy consumption patterns. The problem is particularly complex for older buildings retrofitted with Building Energy Management Systems (BEMS) where extracting useful knowledge from large sensor data streams without full understanding of the underlying system variables is challenging. This paper presents an application of Q-Analysis, a computationally simple topological approach for summarizing large sensor data sets and revealing useful relationships between different variables. Q-Analysis can be used to extract novel structural features called Q-vectors. The Q-vector magnitude visualizations are shown to be very effective in providing insights on macro behaviors, i.e., building floor behaviors in the present case, which are not evident from the use of unsupervised learning algorithms applied on individual terminal units. It has been shown that the building floors exhibited distinct behaviors that are dependent on the set-point distribution, but independent of the time and season of the year.
KeywordsBEMS; HVAC; Internet of Things; computational topology; Q-Analysis; knowledge discovery; data mining
Year2020
JournalSensors
Journal citation20 (17), p. e4914
PublisherMDPI
ISSN1424-8220
Digital Object Identifier (DOI)https://doi.org/10.3390/s20174914
Funder/ClientInnovate UK
Publication dates
Online31 Aug 2020
Publication process dates
Accepted18 Aug 2020
Deposited03 Sep 2020
Publisher's version
License
File Access Level
Open
Licensehttps://creativecommons.org/licenses/by/4.0/
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sensors-20-04914-v2.pdf
License: CC BY 4.0
File access level: Open

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