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Sanitation and Analysis of Operation Data in Energy Systems

Author

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  • Gerhard Zucker

    (AIT Austrian Institute of Technology, Giefinggasse 2, Vienna 1210, Austria)

  • Usman Habib

    (AIT Austrian Institute of Technology, Giefinggasse 2, Vienna 1210, Austria)

  • Max Blöchle

    (AIT Austrian Institute of Technology, Giefinggasse 2, Vienna 1210, Austria)

  • Florian Judex

    (AIT Austrian Institute of Technology, Giefinggasse 2, Vienna 1210, Austria)

  • Thomas Leber

    (Omnetric GmbH, Ruthnergasse 3, Vienna 1210, Austria)

Abstract

We present a workflow for data sanitation and analysis of operation data with the goal of increasing energy efficiency and reliability in the operation of building-related energy systems. The workflow makes use of machine learning algorithms and innovative visualizations. The environment, in which monitoring data for energy systems are created, requires low configuration effort for data analysis. Therefore the focus lies on methods that operate automatically and require little or no configuration. As a result a generic workflow is created that is applicable to various energy-related time series data; it starts with data accessibility, followed by automated detection of duty cycles where applicable. The detection of outliers in the data and the sanitation of gaps ensure that the data quality is sufficient for an analysis by domain experts, in our case the analysis of system energy efficiency. To prove the feasibility of the approach, the sanitation and analysis workflow is implemented and applied to the recorded data of a solar driven adsorption chiller.

Suggested Citation

  • Gerhard Zucker & Usman Habib & Max Blöchle & Florian Judex & Thomas Leber, 2015. "Sanitation and Analysis of Operation Data in Energy Systems," Energies, MDPI, vol. 8(11), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12337-12794:d:58629
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    References listed on IDEAS

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    1. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    2. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    3. Yu, F.W. & Chan, K.T., 2012. "Improved energy management of chiller systems by multivariate and data envelopment analyses," Applied Energy, Elsevier, vol. 92(C), pages 168-174.
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    Cited by:

    1. 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.

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