Comparative analysis of wind speed prediction: enhancing accuracy using PCA and linear regression vs. GPR, SVR, and RNN

For power systems with significant wind power integration to operate in an efficient and dependable manner, wind speed prediction accuracy is crucial. Factors such as temperature, humidity, air pressure, and wind intensity heavily influence wind speed, adding complexity to the prediction process. This paper introduces a method for wind speed forecasting that utilizes principal component analysis (PCA) to reduce dimensionality and linear regression for the prediction model. PCA is employed to identify key features from the extensive meteorological data, which are subsequently used as inputs for the Linear Regression model to estimate wind speed. The proposed approach is tested using publicly available meteorological data, focusing on variables such as temperature, air pressure, and humidity. Popular models like recurrent neural networks (RNN), support vector regression (SVR), and Gaussian process regression (GPR) are used to compare its performance. Evaluation metrics such as root mean square error (RMSE) and R² are used to measure effectiveness. Results show that the PCA combined with Linear Regression model yields more accurate predictions, with an RMSE of 94.11 and R² of 0.9755, surpassing the GPR, SVR, and RNN models.