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International Journal of Forecasting 24 (2008) 694–709
ate/ijforecast
Short-term wind power forecasting using evolutionary algorithms
for the automated specification of artificial intelligence models
⁎
René Jursa , Kurt Rohrig 1
Institut für Solare ungstechnik (ISET) ., Königstor 59, D-34119 Kassel, Germany
Abstract
Wind energy is having an increasing influence on the energy supply in many countries, but in contrast to conventional
power plants, it is a fluctuating energy source. For its integration into the electricity supply structure, it is necessary to predict
the wind power hours or days ahead. There are models based on physical, statistical and artificial intelligence approaches for
the prediction of wind power. This paper introduces a new short-term prediction method based on the application of
evolutionary optimization algorithms for the automated specification of two well-known time series prediction models, .,
works and the nearest neighbour search. Two optimization algorithms are applied pared, namely particle
swarm optimization and differential evolution. To predict the power output of a certain wind farm, this method uses predicted
weather data and historic power data of that wind farm, as well as historic power data of other wind farms far from the
location of the wind farm considered. Using these optimization algorithms, we get a reduction of the prediction error
compared to the model based on works with standard manually selected variables. An additional reduction in error
can be obtained by using the mean model output of the work model and of the nearest neighbour search based
prediction approach.
© 2008 International Institute of Forecasters. Published by Elsevier . All rights reserved.
Keywords: Variable selection; Multivariate time series; works; Nearest neighbour search; Evolutionary optimization; Comparative
studies; Wind energy
1. Introduction accounts for about 6% of the electricity