A novel analysis of random forest regression model for wind speed forecasting

Abstract

This article uses a random forest regression (RFR) model to analyze wind speed forecasting. Wind energy is one of the more critical potentials in renewable energy sources for producing a clean and safe environment. Accurate and stable wind forecasting is essential to improving the efficiency of wind turbines, guaranteeing the power balance, economic dispatch of power systems and ensuring equipment safety. Previous researchers have attempted to address these issues of less wind prediction performance and lack of interpretable analysis. This study aims to develop machine learning (ML) models, such as neural networks (NNs), linear regression (LR), support vector regression (SVR), decision tree regression (DTR), K-nearest neighbors (K-NN), extreme gradient boosting regression and RFR. Six evaluation criteria are applied to estimate the efficiency of the ML model: mean squared error, root mean squared error, mean absolute error (MAE), mean absolute percentage error, normalized average squares of the error and coefficient of determination. The experimental results show the RFR model achieves better prediction accuracy than other models. The forecasting accuracy of the RFR model from wind speed was NMSE = 0.003, MAE = 0.049, MSE = 0.033, RMSE = 0.182, MAPE = 1.180 and R2 = 0.996. Precise wind speed predictions are essential for various industries, such as aviation, shipping and wind energy generation.

Keywords

• Machine-learning models
• random forest regression
• renewable energy sources
• wind energy
• wind speed
• wind speed forecasting