Predicting the energy output of wind farms based on weather data: Important variables and their correlation

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@Article{Vladislavleva:2013:RE,

• author = "Ekaterina Vladislavleva and Tobias Friedrich and Frank Neumann and Markus Wagner",
• title = "Predicting the energy output of wind farms based on weather data: Important variables and their correlation",
• journal = "Renewable Energy",
• volume = "50",
• year = "2013",
• pages = "236--243",
• keywords = "genetic algorithms, genetic programming, Wind energy, Prediction, Data Modeller",
• ISSN = "0960-1481",
• URL = "http://www.sciencedirect.com/science/article/pii/S0960148112003874",
• DOI = "doi:10.1016/j.renene.2012.06.036",
• size = "8 pages",
• abstract = "Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyse the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data.",
• notes = "See oai:arXiv.org:1109.1922 http://arxiv.org/abs/1109.1922",

}

Genetic Programming entries for Ekaterina (Katya) Vladislavleva Tobias Friedrich Frank Neumann Markus Wagner

Citations