Janosch Henze

Address University of Kassel
Intelligent Embedded Systems
Wilhelmshöher Allee 73
34121 Kassel
Germany
Room 0306
Telephone +49 561 804 6679

[2020] [2019] [2018] [2017] [2016]

2020 [to top]

  • Henze, J., Schreiber, J., Sick, B.: Representation Learning in Power Time Series Forecasting. In: Pedrycz, W. and Chen, S.-M. (eds.) Deep Learning: Algorithms and Applications. p. 67--101. Springer International Publishing (2020).
     
  • Henze, J., Siefert, M., Bremicker-Trübelhorn, S., Asanalieva, N., Sick, B.: Probabilistic upscaling and aggregation of wind power forecasts. Energy, Sustainability and Society. 10, 15 (2020).
     

2019 [to top]

  • König, I., Heilmann, E., Henze, J., David, K., Wetzel, H., Sick, B.: Using grid supporting flexibility in electricity distribution networks. In: David, K., Geihs, K., Lange, M., and Stumme, G. (eds.) INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik -- Informatik für Gesellschaft. p. 531--544. Gesellschaft für Informatik e.V., Bonn (2019).
     

2018 [to top]

  • Henze, J., Kutzner, S., Sick, B.: Sampling Strategies for Representative Time Series in Load Flow Calculations. Data Analytics for Renewable Energy Integration. Technologies, Systems and Society - 6th ECML PKDD Workshop, DARE 2018, Dublin, Ireland, September 10, 2018, Revised Selected Papers. p. 27--48 (2018).
     

2017 [to top]

  • Henze, J., Kneiske, T., Braun, M., Sick, B.: Identifying Representative Load Time Series for Load Flow Calculations. Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. p. 83--93. Springer International Publishing, Cham, Switzerland (2017).
     
  • Henze, J., Kneiske, T., Braun, M., Sick, B.: Identifying Representative Load Time Series for Load Flow Calculations. In: Woon, W.L., Aung, Z., Kramer, O., and Madnick, S. (eds.) Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy. p. 83--93. Springer International Publishing, Cham (2017).
     

2016 [to top]

  • Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks. Systems, Man and Cybernetics (SMC), 2016 IEEE International Conference on. p. 2858--2865. IEEE, Budapest, Hungary (2016).