Predicting the Energy Output of Wind Farms Based on Weather Data:
  Important Variables and their Correlation
  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 condition
present at the wind farm. If the output can be predicted more accurately,
energy suppliers can coordinate the collaborative production of different
energy sources more efficiently to avoid costly overproductions.
  With 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 reveal 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 given weather data.
