Performance Evaluation of Feed forward ANN Architectures Based Wind Speed Prediction for India

Predicting wind speed (WS) is essential for evaluating energy resources, especially when it comes to maximizing wind turbine performance and minimizing environmental effects. This study modelswind speed as a function of several meteorological characteristics for Indian sites using machine learning techniques, with an emphasis on Feed forward Neural Network with varying hidden layers and dropout mechanisms. The accuracy is evaluated by evaluation metric. It is found that Feed forward Neural Network with dropout mechanisms is important for WS prediction for Indian site. The findings provide a foundation for future research to explore advanced ANN techniques and hybrid models to further enhance prediction accuracy and support renewable energy integration.