Renewable energy plays a crucial role in reducing carbon emissions and mitigating the impacts of climate change. Wind energy has emerged as a critical contributor to the sustainable energy transition among various renewable sources. However, integrating electric power grids can be difficult because they are unstable due to multiple factors, including the weather, time of day, and location. To overcome these challenges, it is essential to have reliable and flexible energy management approaches that can accurately forecast wind energy. Artificial neural networks are valuable for developing intelligent wind energy prediction models in this context. This book chapter aims to improve wind power generation forecasting by using four input parameters: wind speed, wind direction, pressure, and air temperature from a Texas wind turbine. The proposed model integrates neural network architectures with fractional-order calculus to enhance the accuracy of wind energy predictions. Fractional-order calculus plays a vital role in the proposed model by transforming conventional activation functions into fractional-order activation functions. This transformation leads to improved forecasting accuracy, enabling more precise predictions of wind energy generation. To evaluate the performance of the enhanced model, a comparison is made against a traditional model using metrics such as mean square error and coefficient of determination. It provides valuable insights into the effectiveness of fractional-order activation functions in improving wind power forecasting accuracy.