2023[ to top ]
  • Beinert, D., Holzhüter, C., Thomas, J., Vogt, S.: Power flow forecasts at transmission grid nodes using Graph Neural Networks Energy and AI. 14, 100262 (2023).
2022[ to top ]
  • Nivarthi, C.P., Vogt, S., Sick, B.: Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting In: International Conference on Machine Learning and Applications (ICMLA). bll 1530–1536. IEEE (2022).
2021[ to top ]
  • Huang, Z., He, Y., Vogt, S., Sick, B.: Uncertainty and Utility Sampling with Pre-Clustering In: Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
  • Gruhl, C., Hannan, A., Huang, Z., Nivarthi, C., Vogt, S.: The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models In: Workshop on Self-Improving System Integration (SISSY), ACSOS. bll 204–209. IEEE (2021).
  • Schreiber, J., Vogt, S., Sick, B.: Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast In: European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD): Applied Data Science Track. bll 118–134. Springer (2021).
2020[ to top ]
  • 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).
  • 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).
  • 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 ]
  • Vogt, S., Braun, A., Dobschinski, J., Sick, B.: Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning In: Wind Integration Workshop. , Dublin, Ireland (2019).
  • Vogt, S., Berkhout, V., Lutz, M.-A., Zhou, Q.: Deep Learning Based Failure Prediction in Wind Turbines Using SCADA Data In: Conference for Wind Power Drives (CWD). bll 391–404 (2019).
  • 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 ]
  • Vogt, S., Otterson, S., Berkhout, V.: Multi-task distribution learning approach to anomaly detection of operational states of wind turbines In: Journal of Physics: Conference Series. bl 012040 (2018).
  • Vogt, S., Braun, A., Koch, J., Jost, D., Dobschinski, J.: Benchmark of Spatio-temporal Shortest-Term Wind Power Forecast Models In: Wind Integration Workshop (2018).
  • 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 ]
  • 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 ]
  • Gensler, A., Sick, B., Vogt, S.: A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms In: IEEE Symposium Series on Computational Intelligence (SSCI). bll 1–9. IEEE, Athens, Greece (2016).
2015[ to top ]
  • 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).