Genetic Programming for Wind Power Forecasting and Ramp Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/sgai/Martinez-ArellanoN13,
  author =       "Giovanna Martinez-Arellano and Lars Nolle",
  title =        "Genetic Programming for Wind Power Forecasting and
                 Ramp Detection",
  booktitle =    "Proceedings of the Thirty-third SGAI International
                 Conference on Innovative Techniques and Applications of
                 Artificial Intelligence (AI 2013)",
  year =         "2013",
  editor =       "Max Bramer and Miltos Petridis",
  pages =        "403--417",
  address =      "Cambridge, UK",
  month =        dec # " 10-12",
  organisation = "British Computer Society's Specialist Group on
                 Artificial Intelligence",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2013-12-08",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/sgai/sgai2013.html#Martinez-ArellanoN13",
  language =     "English",
  isbn13 =       "978-3-319-02620-6",
  URL =          "http://dx.doi.org/10.1007/978-3-319-02621-3",
  URL =          "http://dx.doi.org/10.1007/978-3-319-02621-3_30",
  DOI =          "doi:10.1007/978-3-319-02621-3_30",
  abstract =     "In order to incorporate large amounts of wind power
                 into the electric grid, it is necessary to provide grid
                 operators with wind power forecasts for the day ahead,
                 especially when managing extreme situations: rapid
                 changes in power output of a wind farm. These so-called
                 ramp events are complex and difficult to forecast.
                 Hence, they introduce a high risk of instability to the
                 power grid. Therefore, the development of reliable ramp
                 prediction methods is of great interest to grid
                 operators. Forecasting ramps for the day ahead requires
                 wind power forecasts, which usually involve numerical
                 weather prediction models at very high resolutions.
                 This is resource and time consuming. This paper
                 introduces a novel approach for short-term wind power
                 prediction by combining the Weather Research and
                 Forecasting advanced Research WRF model (WRF-ARW) with
                 genetic programming. The latter is used for the final
                 downscaling step and as a prediction technique,
                 estimating the total hourly power output for the day
                 ahead at a wind farm located in Galicia, Spain. The
                 accuracy of the predictions is above 85 percent of the
                 total power capacity of the wind farm, which is
                 comparable to computationally more expensive
                 state-of-the-art methods. Finally, a ramp detection
                 algorithm is applied to the power forecast to identify
                 the time and magnitude of possible ramp events. The
                 proposed method clearly outperformed existing ramp
                 prediction approaches.",
  notes =        "http://www.bcs-sgai.org/ai2013/

                 Research and Development in Intelligent Systems XXX,
                 Incorporating Applications and Innovations in
                 Intelligent Systems XXI. Proceedings of AI-2013, The
                 Thirty-third SGAI International Conference on
                 Innovative Techniques and Applications of Artificial
                 Intelligence

                 Nottingham Trent University, UK",
}

Genetic Programming entries for Giovanna Martinez-Arellano Lars Nolle

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