Forecasting Wind Power for the Day-Ahead Market using Numerical Weather Models and Computational Intelligence Techniques

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  author =       "Giovanna {Martinez Arellano}",
  title =        "Forecasting Wind Power for the Day-Ahead Market using
                 Numerical Weather Models and Computational Intelligence
  school =       "School of Science and Technology, Nottingham Trent
  year =         "2015",
  address =      "Nottingham, NG1 4BU, UK",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "259 pages",
  abstract =     "Wind power forecasting is essential for the
                 integration of large amounts of wind power into the
                 electric grid, especially during large rapid changes of
                 wind generation. These changes, known as ramp events,
                 may cause instability in the power grid. Therefore,
                 detailed information of future ramp events could
                 potentially improve the backup allocation process
                 during the Day Ahead (DA) market (12 to 36 hours before
                 the actual operation), allowing the reduction of
                 resources needed, costs and environmental impact. It is
                 well established in the literature that meteorological
                 models are necessary when forecasting more than six
                 hours into the future. Most state-of-the-art
                 forecasting tools use a combination of Numerical
                 Weather Prediction (NWP) forecasts and observations to
                 estimate the power output of a single wind turbine or a
                 whole wind farm. Although NWP systems can model
                 meteorological processes that are related to large
                 changes in wind power, these might be misplaced i.e. in
                 the wrong physical position. A standard way to quantify
                 such errors is by the use of NWP ensembles. However,
                 these are computationally expensive. Here, an
                 alternative is to use spatial fields, which are used to
                 explore different numerical grid points to quantify
                 variability. This strategy can achieve comparable
                 results to typical numerical ensembles, which makes it
                 a potential candidate for ramp characterisation.",
  notes =        "'A Genetic Programming Approach for Wind Speed

                 'Wind Power Forecasting with Genetic

                 'Appendix F: The Wind Variability of Galicia'",

Genetic Programming entries for Giovanna Martinez-Arellano