Performance Enhancement of Wind Power Forecast System Using AI Approach

The increasing demand for renewable energy sources, coupled with the intermittent nature of wind power, has led to the need for accurate wind power forecasting systems. This paper presents an in-depth investigation into the application of artificial intelligence (AI) approaches in developing a robust wind power forecast system. “Performance Enhancement of Wind Power Forecast Model Using Novel Pre-processing Strategy and Hybrid Optimization Approach.” this work focuses on improving the accuracy of wind power forecasting models, considering the increasing significance of wind energy due to environmental concerns and energy crises. As wind power generation is influenced by variability, accurate forecasting becomes essential for effective planning, operation, and control of power systems. The primary objective of the study is to enhance the accuracy of wind power predictions through innovative preprocessing techniques and a hybrid optimization algorithm. The proposed approach integrates data augmentation, feature extraction, and optimization to achieve improved forecasting results. The proposed hybrid optimization approach is the combination of two optimization algorithms, such as AO and SSA, for feature selection. The hybrid optimization approach effectively chooses the most pertinent features for WPF by combining these two methods. From the selected features the EHNM Model is suggested for WPF. An EHNM Model, which combines a CNN, LSTM network model, and an O-ANN, is used to forecast wind power. The main part of the ensemble is an O-ANN, which is trained using the best features chosen and then fine-tuned using a hybrid optimization approach (AO+SSA). The weighted mean of the three deep learning classifiers—the LSTM network model, CNN, and O-ANN—makes up the wind power forecast result.