Abstract
Wind power forecasting is the need of the era. As wind power generation lays a platform for usage of renewable resources. Employing efficient wind turbines enhances the forecasting of generation in short term. However, power generation differs from conventional thermal due to its unstable nature. Henceforth, accurate wind power forecasting plays in managing the variance in supply and demand of the high energy consumption sectors. This paper deals with classifiers to minimize the errors during wind power forecasting due to some data loss. The proposed paper combines fuzzy logic and neural network to get the better result than the existing methods.