Hybrid fuzzy clustering neural networks to wind power generation forecasting

Wind power forecasting methods can be used to plan unit commitment, scheduling and dispatch by system operators and electricity traders. Because wind power is weather dependent, and therefore, is variable and intermittent over various time-scales, an accurate forecasting of wind power is recognized as a major contribution for a reliable large-scale wind power integration taking profit of economics gains. This paper explores a new approach using fuzzy clustering algorithms for obtaining one day forecast for the characteristics curves of speed wind. Moreover, a Feedforward Neural Networks (FNN) provides an estimate of the average hourly wind speed, for 24 hours horizon.