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A time series clustering approach for Building Automation and Control Systems

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  • Bode, Gerrit
  • Schreiber, Thomas
  • Baranski, Marc
  • Müller, Dirk

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.

Suggested Citation

  • Bode, Gerrit & Schreiber, Thomas & Baranski, Marc & Müller, Dirk, 2019. "A time series clustering approach for Building Automation and Control Systems," Applied Energy, Elsevier, vol. 238(C), pages 1337-1345.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:1337-1345
    DOI: 10.1016/j.apenergy.2019.01.196
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    References listed on IDEAS

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    Cited by:

    1. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    2. Schreiber, Thomas & Netsch, Christoph & Eschweiler, Sören & Wang, Tianyuan & Storek, Thomas & Baranski, Marc & Müller, Dirk, 2021. "Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system," Energy, Elsevier, vol. 230(C).

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