Very Short-Term Wind Speed Prediction Techniques Using Machine Learning

Abstract

Wind speed prediction plays an essential role in planning and operation of wind power systems. An accurate wind speed prediction can reduce costs and enhance the proper use of resources. Wind speed series have high nonlinearity and volatility. In this paper, data-driven models using machine learning (ML) algorithms have been developed to predict a very short-term wind speed. Historical wind speeds lagging up to 20 minutes with 1 minute time interval are used to predict the current and future (up to 5 minutes with L-minnte interval) wind speed. A performance comparative analysis of four ML algorithms including Multiple-Layer Perception Regressor (MLPR), Random Forest Regressor (RFR), K-nearest Neighbors Regressor (KNNR), and Decision Tree Regressor (DTR), is conducted, and their accuracy is evaluated by their R2 values, mean absolute error (MAE), standard deviation (SD) of MAE, mean absolute percentage error (MAPE), SD of MAPE and root mean square error (RMSE). It is found that MLPR gives the best prediction accuracy of 95.3%.