European Journal of Technique, vol.9, no.1, pp.74-83, 2019 (Refereed Journals of Other Institutions)
An area's wind speed forecasting is very
important to investigate whether the area is available for wind power. The wind
speed estimation has been carried out by means of other machine learning
methods, mostly artificial neural networks. Because, in such methods, it is
aimed to estimate the wind speed with the highest accuracy along with the
decimal. However, if a wind farm is to be installed, the wind speed, which is a
variable in the range of 0-20 m / s, can easily be estimated with round values.
If the wind speed values obtained with round values are forecasted with a
high accuracy rate, the wind speed that is necessary for the establishment of a
wind power plant in a region is obtained by a shorter and easier way. In this
study, the decision tree method was used in order to reach wind speed
information with an easier method and with a very short training period.
Decision tree methods were examined in three different structures and three
different decision tree models were designed. Additionally the estimation
results of all three methods were very high, the most accurate estimation was
obtained by the “Coarse Decision Tree” method which is much simpler and faster
than the others.