• O. Kramer, F. Gieseke, and B. Satzger, “Wind energy prediction and monitoring with neural computation”, Neurocomputing, vol. 109, pp. 84-93, 2013. [PDF], [BIB]
  • O. Kramer, F. Gieseke, J. Heinermann, J. Poloczek, and N. A. Treiber”, A Framework for Data Mining in Wind Power Time Series”, in Proc. Data Analytics for Renewable Energy Integration - Second ECML PKDD Workshop, DARE 2014, Nancy, France, September 19, 2014, Revised Selected Papers, 2014, pp. 97-107. [BIB]
  • N. A. Treiber, S. Späth, J. Heinermann, L. von Bremen, and O. Kramer, “Comparison of Numerical Models and Statistical Learning for Wind Speed Prediction”, in Proc. 23rd European Symposium on Artificial Neural Networks, ESANN 2015, Bruges, Belgium, 2015, pp. 71-76. [BIB]
  • N. A. Treiber and O. Kramer, “Evolutionary feature weighting for wind power prediction with nearest neighbor regression”, in Proc. IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, pp. 332-337. [BIB]
  • J. Heinermann and O. Kramer, “Machine Learning Ensembles for Wind Power Prediction”, Renewable Energy, vol. 2016. [BIB]
  • J. Heinermann and O. Kramer, “Precise Wind Power Prediction with SVM Ensemble Regression”, in Proc. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, 2014, pp. 797-804. [BIB]
  • N. A. Treiber and O. Kramer, “Wind Power Prediction with Cross-Correlation Weighted Nearest Neighbors”, in Proc. 28th International Conference on Informatics for Environmental Protection: ICT for Energy Effieciency, EnviroInfo 2014, Oldenburg, Germany, September 10-12, 2014., 2014, pp. 63-68. [BIB]
  • J. Poloczek, N. A. Treiber, and O. Kramer, “KNN Regression as Geo-Imputation Method for Spatio-Temporal Wind Data”, in Proc. International Joint Conference SOCO‘14-CISIS‘14-ICEUTE‘14 - Bilbao, Spain, June 25th-27th, 2014, Proceedings, 2014, pp. 185-193. [BIB]

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