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@ARTICLE{Zimmer:1038165,
      author       = {Zimmer, Marcel and Buechel, Maximilian and Redder, Florian
                      and Mork, Maximilian and Pesch, Thiemo and Xhonneux, André
                      and Müller, Dirk and Benigni, Andrea},
      title        = {{D}igital {T}wins for {B}uilding {P}seudo-{M}easurements},
      journal      = {IEEE transactions on instrumentation and measurement},
      volume       = {74},
      issn         = {0018-9456},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-01212},
      pages        = {2504510},
      year         = {2025},
      abstract     = {Building control architectures are strongly limited by the
                      systematic lack of measurements at user-relevant locations.
                      This paper proposes a digital twin architecture grounded in
                      Correlated Gaussian Processes (Corr-GP) that provide
                      information in the form of pseudo-measurements. Tested with
                      thermal and CO2 measurements collected from the field,
                      close-to-person pseudo-measurements are provided based on
                      the continuous input of remotely located measurement
                      signals. In particular, detailed short-term as well as
                      long-term results are provided for both temperature and CO2
                      digital twins. We show that the proposed approach is
                      trainable on only a few days of measurements. This property
                      makes the proposed approach especially useful in field
                      applications, where alternative algorithms, such as, for
                      example, neural network architectures, are not capable of
                      dealing with small amounts of data. We demonstrate how to
                      adjust the proposed approach to provide temperature and CO2
                      digital twins for the generation of pseudo-measurements.
                      Within the given framework, we show how to utilize the
                      proposed digital twin to couple multiple reference sensors
                      to provide close-to-person pseudo-measurements. By extending
                      the Corr-GP approach to a non-zero prior mean formulation,
                      we show how to reduce the included information by the
                      reference sensors. More precisely, the extended approach can
                      be defined as a digital twin with only a single reference
                      sensor. This enables a reliable long-term application by
                      avoiding the need for retraining caused by changing
                      seasonalities within the signal characteristics. That is, we
                      show that the digital twin trained in summer can be operated
                      in winter.},
      cin          = {ICE-1},
      ddc          = {620},
      cid          = {I:(DE-Juel1)ICE-1-20170217},
      pnm          = {1122 - Design, Operation and Digitalization of the Future
                      Energy Grids (POF4-112) / 1123 - Smart Areas and Research
                      Platforms (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1122 / G:(DE-HGF)POF4-1123},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001410586700017},
      doi          = {10.1109/TIM.2025.3527590},
      url          = {https://juser.fz-juelich.de/record/1038165},
}