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

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  • Vladislavleva, Ekaterina
  • Friedrich, Tobias
  • Neumann, Frank
  • Wagner, Markus

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 analyze 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.

Suggested Citation

  • Vladislavleva, Ekaterina & Friedrich, Tobias & Neumann, Frank & Wagner, Markus, 2013. "Predicting the energy output of wind farms based on weather data: Important variables and their correlation," Renewable Energy, Elsevier, vol. 50(C), pages 236-243.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:236-243
    DOI: 10.1016/j.renene.2012.06.036
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    References listed on IDEAS

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    1. Jursa, René & Rohrig, Kurt, 2008. "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 694-709.
    2. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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    Cited by:

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    7. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
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