Performance Analysis of Machine Learning Techniques for Wind Speed Prediction

One of the most potential energy sources in this current world is the energy of wind. It has several advantages like its renewability, cost effectiveness and sustainability make it a great source of energy. But, to predict the power generation from wind is complex process. In this paper, the effectiveness of four machine learning algorithms: random forest regressor, decision tree regressor, gradient boosting regressor, voting regressors have been used for predicting wind speed, which has direct co-relation with power production. For measuring the accuracy of these models three standard criteria namely mean square error, root mean square error and R squared have been used. In this paper, the performance and accuracy of those algorithms have been analyzed for the purpose of predicting wind speed which is one of the most important factors for wind power utilization.