Forecast of wind turbine output power by a multivariate Polynomial-RF model

Based on the SCADA data of a wind farm, this paper proposes a hybrid multivariate prediction model that combines polynomial regression and random forest (RF) to predict wind turbine output power. First, to reduce the difficulty of expert analysis, the Maximum Information Coefficient (MIC) is used to analyze the correlation between data and select features. Wind speed, rotor speed, pitch angle, and wind direction are considered important and chosen for power prediction modeling. Then the relationship between multiple variables and output power is established through polynomial regression. Finally, the new polynomial features and output power are used to train the RF model to predict the output power. The experimental results show that, the predicted wind speed and power diagram shows a band-like distribution, which matches closely with the real one; compared with multiple models such as a single RF model, a polynomial regression model, and a feedforward artificial neural network (ANN) model, the polynomial-RF model has the highest prediction accuracy. The Mean Absolute Percentage Error (MAPE) of the polynomial-RF model prediction is 0.06, and the Root Mean Square Error (RMSE) is 114. Compared with the single RF model, the MAPE predicted by the Polynomial-RF model is reduced by 14%, and the RMSE is reduced by 6%. It can be seen that the Polynomial-RF model proposed in this paper has a good application prospect in the prediction of wind turbine output power.