Monthly average wind speed forecasting in giresun province with fuzzy regression functions approach

In recent years, fuzzy inference systems attract attention as effective methods used to solve forecasting problems. Fuzzy inference systems rely on fuzzy sets and use membership values as well as original data. The fuzzy regression functions approach, which is one of the popular fuzzy inference systems, has a distinct importance from many fuzzy inference systems, unlike many other fuzzy inference systems in the literature, because it does not have a rule base and is easier to apply. In this thesis, both the monthly average wind speed forecasting in Giresun Province is performed for the first time in the literature and the fuzzy regression functions approach method is used for the first time in the literature to forecast the wind speed. In order to evaluate the performance of fuzzy regression functions approach used for monthly average wind speed forecasting in Giresun Province, the results obtained from many methods suggested for forecasting problems in the literature are compared. As a result of the evaluations made, it is concluded that the forecasts obtained by the fuzzy regression functions approach are better than many other methods in the literature.