Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm

Abstract

Due to classification, the dependent variable will have binary variables, and the expectation, E(Yk) , will represent a probability which has values between 0 and 1. [...]the dependent variable would be a binary variable, so it can be sufficient to use in a curve model, but not a linear model. For data on the incorrect side of the margin, the function’s value is relative to the distance from the margin. [...]to analyze the binary data, which had a non-linear character, we used a logistic regression that could analyze non-linear data to make a statistical model. After training, we forecasted wind power outputs over the next 24 h. We obtained the forecasted values by using a SVM based on a multi-variable regression, which was more accurate than the SVM based on single-variable regression (A review of additional turbines is summarized in Appendix A). [...]the proposed method for estimating accuracy and forecasting outputs can provide reliable predictions of wind power outputs to power system operators.