This chapter examines the effectiveness of three neural network architectures—multilayer perceptron (MLP), long short-term memory (LSTM), and gated recurrent unit (GRU)—in every 30 minutes and hourly generating power of a wind farm. The study aims to enhance the accuracy of wind power predictions. We employ a comprehensive methodology that includes data preprocessing, feature engineering, and hyperparameter tuning to evaluate the performance of these models. This chapter provides a detailed description of each neural network architecture utilized in the research. We outline the data processing steps, which encompass data cleaning, correlation analysis, and normalization, as well as the hyperparameter tuning process used to optimize each model’s performance. Our findings indicate that the GRU model outperforms both the LSTM and MLP models in terms of root mean square error (RMSE) for 30-minute and hourly forecasts. We conclude that the GRU model is the most suitable for less-hourly wind power forecasting, particularly when utilizing power and wind speed parameters.