Pu­bli­ka­tio­nen

[ 2021 ] [ 2020 ] [ 2019 ] [ 2018 ]

2021 [ nach oben ]

  • 1.
    Schreiber, J., Vogt, S., Sick, B.: Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast. ECML PKDD 2021 (2021).
     
  • 2.
    Schreiber, J., Sick, B.: Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. International Conference on Pattern Recognition (ICPR) (2021).
     

2020 [ nach oben ]

  • 1.
    Henze, J., Schreiber, J., Sick, B.: Representation Learning in Power Time Series Forecasting. In: Pedrycz, W. en Chen, S.-M. (reds.) Deep Learning: Algorithms and Applications. bll. 67–101. Springer International Publishing (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.
    Deist, S., Schreiber, J., Bieshaar, M., Sick, B.: Extended Coopetitive Soft Gating Ensemble. arXiv e-prints. arXiv:2004.14026 (2020).
     

2019 [ nach oben ]

  • 1.
    Schreiber, J.: Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2018. bll. 75–87. kassel university press, Kassel, Germany (2019).
     
  • 2.
    Schreiber, J., Buschin, A., Sick, B.: Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models. In: David, K., Geihs, K., Lange, M., en Stumme, G. (reds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. bll. 585–598. Gesellschaft für Informatik e.V., Bonn (2019).
     
  • 3.
    Schreiber, J., Jessulat, M., Sick, B.: Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic. In: Tetko, I.V., Krurková, V., Karpov, P., en Theis, F. (reds.) Artificial Neural Networks and Machine Learning -- ICANN 2019: Image Processing. bll. 550–564. Springer International Publishing, Cham (2019).
     

2018 [ nach oben ]

  • 1.
    Schreiber, J., Sick, B.: Quantifying the Influences on Probabilistic Wind Power Forecasts. International Conference on Power and Renewable Energy. bl. 6 (2018).
     
  • 2.
    Deist, S., Bieshaar, M., Schreiber, J., Gensler, A., Sick, B.: Coopetitive Soft Gating Ensemble. Workshop on Self-Improving System Integration (SISSY). , Trento, Italy (2018).