Dr. Martin Felder

  • Senior Cientist, Center for Solar Energy and Hydrogen Research (ZSW)

Email: martin.felder@ideoj.org

Key publications

  1. Matthiss, B., Gaedke, P., Felder, M. and Binder, J.: Probabilistic loadflow methods for energy management schemes in distribution grids. In Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 2017.
  2. Sehnke, F., Strunk, A., Felder, M., Brombach, J., Kaifel, A. and Meis, J.: Wind power resource estimation with deep neural networks. In Proc. of International Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, 2013.
  3. Felder, M., F. Sehnke, A. Kaifel: Automatic feature selection and architecture optimization for neural network based wind power forecasting. In proceedings of DEWEK 2012.
  4. Kaifel, A.K., M. Felder, C. DeClercq, and J-C. Lambert: New Dynamic NNORSY Ozone Profile Climatology. Atmospheric Measurement Techniques Discussions, 2012.
  5. Felder, M., Frank Sehnke, Anton Kaifel: Improving Physical Wind Power Forecasts with Recurrent or Deep Neural Networks. EWEA Annual event 2011 (EWEA 2011), Brussels, Belgium, 2011.
  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. Schaul, T., Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstiess, and Jürgen Schmidhuber. PyBrain. Journal of Machine Learning Research, 2010.
  8. Oddo C., Beccai L., Felder M., Giovacchini F., Carrozza M. Artificial Roughness Encoding with a Bio-inspired MEMS-based Tactile Sensor Array. Sensors 9(5), 3161-3183, 2009.
  9. Rückstieß, M. Felder, J. Schmidhuber: State-Dependent Exploration for Policy Gradient Methods. In: Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008, Antwerp, Belgium, Springer Berlin / Heidelberg, 2008.
  10. Vazquez Rodriguez, L., M. Felder, A. Knoll, A cognitive architecture framework for COTESYS, 1st Int. Conf. Cognition for Technical Systems, Munich, Germany, October 2008.
  11. Felder, M.D., J. Joiner, P. Poli: The impact of ozone field horizontal inhomogeneities on orbital backscatter UV measurements. J. of Geophys. Res., 112, D01303, 2007.
  12. Meijer, Y.J., R.J. van der A, P.K. Bhartia, G.E. Bodeker, K. Chance, L.E. Flynn, H.M. Kelder, B.J. Kerridge, M.D. Felder, R.F. van Oss, M. Weber, C. Zehner, et al.: Evaluation of GOME ozone profiles from nine different algorithms. J. of Geophys. Res., 111, D21306, 2006.
  13. Müller, M.D., P.K. Bhartia, I. Štajner, Assimilation and Validation of Radiances from the Solar Backscatter UltraViolet/2 Instrument, AGU Fall Meeting, San Francisco, December 2004.
  14. Müller, M.D., P.K. Bhartia, I. Štajner, Assimilation of SBUV Version 8 Radiances into the GEOS Ozone DAS, Proc. Quadrennial Ozone Symp. Kos, Greece, June 2004.
  15. Lary, D.J., H.Y. Mussa, M.D. Müller: Using neural networks to describe tracer correlations, Atmosph. Chem. and Phys., 4, 143–146, 2004.
  16. Müller, M.D., A.K. Kaifel, M. Weber, S. Tellmann, J.P. Burrows, D. Loyola: Ozone Profile Retrieval from GOME Data using a Neural Network Approach (NNORSY), J. of Geophys. Res. 106(D16), 4497–4515, 2003.
  17. 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. Soc. 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.
  18. Müller, M.D., A.K. Kaifel, M. Weber, S. Tellmann: Real-time total ozone and ozone profiles retrieved from GOME data using neural networks. In: Proc. 2001.
  19. EUMETSAT Meteorological Satellite Data User’s Conference, Antalya, 1–5 October 2001.
  20. EUMETSAT, Darmstadt, Germany, 2001.
  21. Müller, M.D. and A.K. Kaifel, Efficient processing of multi-year global TOVS data using ITPP, 3I and neural networks, Techn. Proc. 10th Int. TOVS Study Conference, pp. 397–407, Boulder, CO, USA, 1999.