Wind Power Forecast Model Performance Enhancement System Using Hybrid Approach

This study addresses the critical role of WPF in ensuring stable and reliable power system operations. Due to its dependency on dynamic climatic conditions and associated factors, accurate WPF is challenging. Titled "Wind Power Forecast Model Performance Enhancement Using Hybrid Approach" this research delves into various aspects, including input data, input selection techniques, data pre-processing, and forecasting methods, with the aim to design highly efficient online/offline models for improving the accuracy of wind power forecasting. The overarching goal is to enhance the reliability and stability of wind power systems. The analysis reveals that proposed hybrid models offer more accurate results, highlighting their significance in the current era. This approach combines the CSO & HBA for feature selection, effectively identifying the most relevant features for WPF. The selected features are then utilized in the HBCro Model, a combination of CSO and HBA network models with a RNN. The main backbone of the WPF model is an RNN, which is trained selecting a best features chosen and then it is fine-tuned using a hybrid optimization approach for forecasting. RNN makes up the wind power forecast result.