A time series clustering approach for Building Automation and Control Systems

Gerrit Bode, Thomas Schreiber, Marc Baranski and Dirk Müller

Applied Energy, 2019, vol. 238, issue C, 1337-1345

Abstract: Structured data of all sensors and actuators are a requirement for decisions about control strategies and efficiency optimization in Building Automation. In practice, the analysis of data is a challenging and time-consuming task. In previous work, it has been demonstrated that classification algorithms may reach high classification accuracies when applied to building data. However, supervised algorithms require labelled training data sets and a predefined classes, and depend highly on the selection of input features.

Keywords: Big data; Unsupervised; Machine learning; Building automation and control; Time series clustering; Feature extraction (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2019.01.196

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