Pub­lic­a­tions

[ 2021 ] [ 2020 ] [ 2019 ] [ 2018 ] [ 2017 ] [ 2016 ] [ 2015 ]

2021 [ to top ]

  • 1.
    Huang, Z., He, Y., Vogt, S., Sick, B.: Uncertainty and Utility Sampling with Pre-Clustering. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
     
  • 2.
    Gruhl, C., Hannan, A., Huang, Z., Nivarthi, C., Vogt, S.: The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models. Workshop on Self-Improving System Integration (SISSY), ACSOS. bll. 204–209. IEEE (2021).
     
  • 3.
    Schreiber, J., Vogt, S., Sick, B.: Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track. bll. 118–134. Springer (2021).
     

2020 [ to top ]

  • 1.
    Bremicker-Trübelhorn, S., Vogt, S., Siefert, M.: Statistical correction scheme for the wind power allocation to transformer stations in the transmission grid. Electric Power Systems Research. 189, 106623 (2020).
     
  • 2.
    Bieshaar, M., Schreiber, J., Vogt, S., Gensler, A., Sick, B.: Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets. arXiv e-prints. arXiv:2010.05898 (2020).
     
  • 3.
    Lutz, M.-A., Vogt, S., Berkhout, V., Faulstich, S., Dienst, S., Steinmetz, U., Gück, C., Ortega, A.: Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data. Energies. 13, 1063 (2020).
     

2019 [ to top ]

  • 1.
    Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning. Wind Integration Workshop. , Dublin, Ireland (2019).
     
  • 2.
    Vogt, S., Berkhout, V., Lutz, M.-A., Zhou, Q.: Deep Learning Based Failure Prediction in Wind Turbines Using SCADA Data. Conference for Wind Power Drives (CWD). bll. 391–404 (2019).
     
  • 3.
    Saint-Drenan, Y.-M., Vogt, S., Killinger, S., Bright, J.M., Fritz, R., Potthast, R.: Bayesian parameterisation of a regional photovoltaic model--Application to forecasting. Solar Energy. 188, 760–774 (2019).
     

2018 [ to top ]

  • 1.
    Vogt, S., Otterson, S., Berkhout, V.: Multi-task distribution learning approach to anomaly detection of operational states of wind turbines. Journal of Physics: Conference Series. bl. 012040 (2018).
     
  • 2.
    Vogt, S., Braun, A., Koch, J., Jost, D., Dobschinski, J.: Benchmark of Spatio-temporal Shortest-Term Wind Power Forecast Models. Wind Integration Workshop (2018).
     
  • 3.
    Gensler, A., Sick, B., Vogt, S.: A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies. Renewable and Sustainable Energy Reviews. 96, 352–379 (2018).
     

2017 [ to top ]

  • 1.
    Heppelmann, T., Steiner, A., Vogt, S.: Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle. Meteorologische Zeitschrift. 26, 319–331 (2017).
     

2016 [ to top ]

  • 1.
    Gensler, A., Sick, B., Vogt, S.: A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms. IEEE Symposium Series on Computational Intelligence (SSCI). bll. 1–9. IEEE, Athens, Greece (2016).
     

2015 [ to top ]

  • 1.
    Saint-Drenan, Y.-M., Bofinger, S., Fritz, R., Vogt, S., Good, G., Dobschinski, J.: An empirical approach to parameterizing photovoltaic plants for power forecasting and simulation. Solar Energy. 120, 479–493 (2015).