Regional Wind Power Forecasting Using Bayesian Feature Selection And Machine Learning Techniques

The rapid growth of renewable energy sources has increased the importance of accurate wind power forecasting for reliable power system operation. Wind power generation is inherently variable due to changing weather conditions, making prediction a challenging task. This paper presents an intelligent wind power forecasting framework based on Bayesian Feature Selection combined with machine learning models. The proposed approach processes numerical weather prediction data and removes irrelevant spatial features to improve prediction accuracy. A dimensionality reduction technique is applied to select the most informative sub-areas of weather data, thereby reducing computational complexity while maintaining important predictive information. Various machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, and Convolutional Neural Networks are employed for forecasting regional wind power output. The proposed model enhances prediction performance by optimizing feature selection and improving model efficiency. Experimental evaluation demonstrates that the system significantly improves forecasting accuracy while reducing the dimensionality of input data. The framework can assist energy providers and power grid operators in planning and managing renewable energy resources more effectively.