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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@PhdThesis{Martinez-Arellano:thesis,

• author = "Giovanna {Martinez Arellano}",
• title = "Forecasting Wind Power for the Day-Ahead Market using Numerical Weather Models and Computational Intelligence Techniques",
• school = "School of Science and Technology, Nottingham Trent University",
• year = "2015",
• address = "Nottingham, NG1 4BU, UK",
• month = apr,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://irep.ntu.ac.uk/id/eprint/322/",
• URL = "http://irep.ntu.ac.uk/id/eprint/322/1/220627_Giovanna.Martinez-2015excl.3rdpartymaterial.pdf",
• 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 Downscaling'

  'Wind Power Forecasting with Genetic Programming'

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

}

Genetic Programming entries for Giovanna Martinez-Arellano

Citations