Wind power plant forecasting and power prediction methods using Machine Learning Algorithms

Abstract

A significant and eligible source such as wind energy has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas. In this study, wind power forecasting was performed based on daily wind speed data using machine learning algorithms. The proposed method is based on machine learning algorithms to forecast wind power values efficiently. Tests were conducted on data sets to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, several machine learning models were built for analysis on the accuracy level of the respective models, i.e, the accuracy levels of the machine.