Fuzzy rough regression with application to wind speed prediction

Accurate wind speed prediction is a prerequisite of large-scale wind power generation. There are several uncertain factors which degrade the performance of the current wind speed prediction systems. Fuzzy rough sets are considered as a powerful tool to deal with uncertainty, and have been widely discussed and applied in classification learning. In this work we describe a regression algorithm based on fuzzy rough sets, consisting of fuzzy partition, fuzzy approximation and estimation of regression values. In this algorithm, the training set is divided into k fuzzy classes with fuzzy partition, and then the predicted values of test samples are determined in the finite intervals with fuzzy rough approximation, finally they are estimated with lower and upper limits of the intervals. Numerical experiments on UCI data sets and wind speed prediction show the effectiveness of the proposed algorithm.