Wind Speed Forecasting Using REPTree and Bagging Methods in Kirklareli-Turkey

In this study, an analysis was performed by examining the wind power potential of Kırklareli province which is in the west of Turkey. Statistical data between 2001 and 2007 was used in this study. The data was obtained from Kırklareli branch of State Meteorological Service. In Kırklareli region, wind speed forecasts regarding the year 2013 were made for windpower plants that are supposed to be built. WEKA tool was used for the performed analyzes. Algorithm which was used for forecasting is REPTree which is decision tree algorithm. There are two basic reasons to use REPTree algorithm. First, it produces better results compared to other machine learning methods, and secondly, the model produced with REPTree has a clear content. For this reason, new information can be gathered by using the tree model. This advantage of REPTree algorithm is combined with Bagging method and average model is generated by using the models produced by new training sets that are derived from the original training set. In this way, the model that will provide the highest accuracy rate is produced. The correlation coefficient value between the real and estimated values is obtained as 0,8154 by applying cross-validation method on the training set. This shows that REPTree can be used along with Bagging method for the wind speed forecasting of the year 2013