Methodology and Precision Research of Wind Farm Power Prediction


  • Rimantas Pranas Deksnys Kaunas University of Technology
  • Aldas Stankevicius



Wind farms, wind power generation, power prediction, prediction precision


The article presents a newly developed statistical regression wind farm power change prediction model. Results of the research and the data analysis performed show that the model is able to evaluate factors determining the wind farm gross output and to increase the prediction accuracy. The influence of the regression equation independent variables on the dependent one is determined by the means of the Students t-test, and the levels of varying equation coefficients are established. The transformation of weather density and wind speed product corresponding to the linear stochastic dependence of the variable power characteristic part for the wind power plant is estimated. Expression of the transformation is suitable to use for predicting the wind farm power in the range from the minimum values to the installed ones. The statistical regression model of the wind farm power prediction is presented on the basis of given technique of the linear regression analysis, the exponential regression equation, and variable coefficients of regression equation. Results of power prediction by the given model precision research show that the one-day relative average prediction error does not exceed 7.52 % of the installed value.





How to Cite

Deksnys, R. P., & Stankevicius, A. (2017). Methodology and Precision Research of Wind Farm Power Prediction. Elektronika Ir Elektrotechnika, 23(1), 49-56.