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 Kirklareli province which is in the west of Turkey. Statistical data between 2001 and 2007 was used in this study. The data was obtained from Kirklareli branch of State Meteorological Service. In Kirklareli 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.

[1]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[2]  Ahmet Erdil,et al.  The prediction of meteorological variables using artificial neural network , 2012, Neural Computing and Applications.

[3]  M. Y. Mashor.  Hybrid training algorithm for RBF Network , 2000 .

[4]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[5]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[6]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[7]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[8]  E. Uçar,et al.  Developing of a learning-based system to assist treatment process of arrhythmia patients , 2011 .

[9]  Fatih Onur Hocaoglu,et al.  Missing wind data forecasting with adaptive neuro-fuzzy inference system , 2009, Neural Computing and Applications.

[10]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[11]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[12]  Robert M. Lewitt,et al.  Practical considerations for 3-D image reconstruction using spherically symmetric volume elements , 1996, IEEE Trans. Medical Imaging.

[13]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[14]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[15]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[16]  Hong-Wen Deng,et al.  Gene selection for classification of microarray data based on the Bayes error , 2007, BMC Bioinformatics.

[17]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[18]  Murat Gökçek,et al.  Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey , 2007 .

[19]  Tahir Cetin Akinci,et al.  Determination of Wind Energy Potential in Kirklareli-Turkey , 2013 .

[20]  Pedro M. Domingos.  A Unifeid Bias-Variance Decomposition and its Applications , 2000, ICML.

[21]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[22]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[23]  A. Bachelor.  GLOSSARY OF TERMS GLOSSARY OF TERMS , 2010 .

[24]  S. Stigler.  Francis Galton's Account of the Invention of Correlation , 1989 .

[25]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[26]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[27]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[28]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[29]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[30]  Nils J. Nilsson,et al.  Introduction to Machine Learning , 2020, Machine Learning for iOS Developers.

[31]  Pedro Antonio Gutiérrez,et al.  Evolutionary product unit neural networks for short-term wind speed forecasting in wind farms , 2012, Neural Computing and Applications.

[32]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..