Wind Speed Forecasting Using Machine Learning Models

Abstract

Wind energy is the strongest renewable energy source which ensures clean and safe production of energy. The wind speed prediction has an important place in wind energy systems and to drive turbines that further helpful for generating electricity, but the issue with the system is that power generated from wind is uncertain. So, accurate wind speed forecasting is required to produce more electric power. To address this issue, many approaches are presented by various researchers. This project describes an empirical study of modeling and forecasting of wind speed of Chennai city.. This project aims to build two wind speed prediction models The mean square error (MSE) and root mean square error (RMSE) are used to compare the performance of Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) in order to determine which model is superior for wind speed forecasting.