Ing. Anton Kaifel

  • Group leader of Simulation and Modeling Working Group at Center for Solar Energy and Hydrogen Research (ZSW)

Email: anton.kaifel@ideoj.org

Key publications

  1. Anger, Jan, Jens Bange, Cornelia Blick, Frederik Brosz, Stefan Emeis, Joachim Fallmann, Martin Felder, et al.
    Erstellung Einer Konzeption Eines Windenergie-Testgeländes in Bergig Komplexem Terrain.
    Abschlussbericht Forschungsvorhagen BMWi Förderkennzeichen: 0325656 A-D. Stuttgart, Deutschland:
    Abschlussbericht Forschungsvorhagen BMWi Förderkennzeichen: 0325656 A-D, 2016.
  2. Sehnke, F., Strunk, A., Felder, M., Brombach, J., Kaifel, A., Meis, J., 2013. Wind Power Resource Estimation with Deep Neural Networks, in: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2013, Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 563–570.
  3. Schröer, R., Kötter, E., Voormann, M., Gaul, A., Erdmann, M., Doll, R., Kaifel, A., Sehnke, F., Ohnmeiss, K., 2014.
    Die Rolle von Power-to-Gas in der zukünftigen Stromversorgung – Das optimierte Stromversorgungsystem bei hohen Anteilen Erneuerbarer Energien am Beispiel der Modellregion Trier- Amprion 5. Abschlussbericht Forschungsvorhaben BMWi Förderkennzeichen: 0325503A & 0325503B.
  4. Sehnke, F., Capota, M., Felder, M.D., Kaifel, A.K: Abschätzung einer Deutschland-Windleistungskurve unter den Leitstudien Annahmen für das Jahr 2050 mit Hilfe der Windleistungsmessungen von 2011 – Internes Papier SYS, ZSW Stuttgart, 2012.
  5. Sack, Jeremy, Achim Strunk, Jon Meis, Frank Sehnke, Martin Felder, and Anton K. Kaifel. “From Ensembles to Probabilistic Wind Power Forecasts – How Crucial Is the Ensemble Size?” ICEM 2013, 2013. http://www.icem2013.org/wp-content/uploads/2013/08/27_JeremySack.pdf.
  6. Felder, M., A.K. Kaifel, A. Graves: Wind Power Prediction using Mixture Density Recurrent Neural Networks. European Wind Energy Conference 2010 (EWEC 2010), Poland, 2010.
  7. Felder, M., F. Sehnke, A. Kaifel: Automatic feature selection and architecture optimization for neural network based wind power forecasting. In proceedings of DEWEK 2012.
  8. Schwander, H., A. Kaifel, A. Ruggaber and P. Koepke, Spectral radiative transfer modelling with minimized computation time by use of neural-network technique, Appl. Opt., 40, 3, 331-335, 2001.
  9. Schwander H., P. Koepke, A. Kaifel, and G. Seckmeyer, Modification of spectral UV irradiance by clouds, J. Geophys. Res., 107 (D16), 4319, doi:10.1029/2001JD001297, 2002.
  10. Müller, M.D., A.K. Kaifel, M. Weber and J.P. Burrows; Partial training of neural networks with incomplete target data, applied to atmospheric science, submitted to Neural Networks (2002).
  11. Kaifel, A., J. Kaptur, O. Reutter, M. Wohlfart, H. Schwander, P. Koepke, K. Dehne, U. Feister, R. Grewe, M. Köhl, F. Brucker: UV-Spectral Radiometer on Filter-Modell-Basis (UV-SPRAFIMO), in UV Ground- and Space-Based Measurements, Models and Effects III, J.R. Slusser, J.R. Herman and W. Gao (Eds.), Proc. SPIE 5156 pp- 343-366, 2003.
  12. Müller, M.D., A.K. Kaifel, M. Weber, J.P. Burrows: A new Method for Retrieving Total Ozone from GOME Data, Applied Optics, 41(24), 5051–5058, 2002.
  13. Müller, M. D., A. K. Kaifel, M. Weber, S. Tellmann, J. P. Burrows, and D. Loyola, Ozone profile retrieval from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY)), J. Geophys. Res., 108(D16), 4497, doi:10.1029/2002JD002784, 2003.
  14. Feister, U., A.K. Kaifel, R. Grewe, J. Kaptur, O. Reutter, M. Wohlfart, K. Gericke, Fast measurements of solar spectral UV irradiance – first performance results of two novel spectrometers. Opt. Engineering, Vol. 44, 2005.
  15. Meijer, Y.; Swart, D.; Baier, F.; Bhartia, P.; Bodecker, G.; Casadio, S.; Chance, K.; Del Frate, F.; Erbertseder, T.; Flynn, L.; Godin-Beekmann, S.; Hansen, G.; Hasekamp, O.; Kaifel, A.; Kelder, H.; Kerridge, B.; Lambert, J.-C.; Landgraf, J.; Latter, B.; Liu, X.; McDermid, S.; Müller, M.; Pachepsky, Y.; Rozanov, V.; Siddans, R.; Tellmann, S.; van der A, R.; van Oss, R.; Weber, M.; Zehner, C.: Evaluation of GOME ozone profiles from nine different algorithms, J. Geophys. Res., 111, D21306, doi:10.1029/2005JD006778, 2005.
  16. Kaifel, A. K., Felder, M., DeClercq, C., and Lambert, J.-C.: New dynamic NNORSY ozone profile climatology, Atmos. Meas. Tech. Discuss., 5, 775-812, doi:10.5194/amtd-5-775-2012, 2012.
  17. Kaifel, A.K. and M.D. Müller, Results of TOVS Ozone Retrieval with Neural Networks, in: J. Le Marshall and J. D. Jasper (eds), Techn. Proc. 11th Int. TOVS Study Conference, Budapest, Bureau of Meteorology Research Centre, Melbourne (2001) 153–165. Müller, M.D., A.K. Kaifel: Ozone profile retrieval from GOME data using a neural network inverse model. In:
    Proc. Third Conference on Artificial Intelligence Applications to Environmental Science, Long Beach, California, February 2003, American Met. Society.
  18. Kaifel, A.K., M. Felder, J.S. Kaptur; NNORSY ozone profile retrieval using combined UV/VIS and IR satellite data. ESA Envisat Symposium 2007, Montreux, ESA, SP-636.
  19. Kaptur, J.S., A.K. Kaifel, Dynamic ozone profile climatology: Software bundle for integration to retrieval schemes, 1st Int. IASI Conf., Biaritz, France, 2007.
  20. Kaifel, A.K., J.S. Kaptur, C. deClercq, J.C. Lambert, T. Ebertseder, NNORSY-GOME Ozone Profile Retrieval Products and Climatology, 16th Int. TOVS Study Conf., Angra dos Reis, Brazil, 2008.