Deep Machine Learning to Predict Wind Speed through Temperature and Humidity

Abstract

The paper aims to predict the wind speed of Karachi based on temperature and Humidity of he city. A deep machine learning technique through a double layer neural network has been employed to analyze the meteorological parameters. The model was trained to capture complex patterns and dynamics and after validation, testing followed. The performance evaluation of the outcomes is verified through statistical parameters like R² and RMSE which comes out as for single and for double layer. It is found that the double-layer results show improvement than the single layer results. The promising results show the potential of deep learning in enhancing the forecasting of wind speed.