Wind power generation forecasting plays a pivotal role in optimizing renewable energy resources. This chapter presents a detailed analysis of predicting wind power output from the Jeju Island wind farm’s three distinct sites (Sites A, B, and C) using FONN models. Employing single-layer and multi-layer FONN models explores the effectiveness of fractional-order activation functions, including fractional LeCun Tanh, ArcTan, and Hard Tansig, compared to their conventional counterparts. Results indicate that fractional functions consistently outperform traditional methods in terms of \(R^2\) and MSE metrics during both the training and testing phases. Specifically, fractional LeCun Tanh demonstrated superior accuracy, achieving minimal error rates across all three sites. The analysis underscores the potential of fractional calculus-enhanced models to improve the reliability of wind energy forecasting.