Wind Power Probability Density Forecasting Method Based On EMD,Error Correction And Bootstrap Quantile Regression

Wind power forecasting is critical to the construction,planning,and production scheduling of current wind power integration and smart grids.Accurate and effective wind power forecasting is an inevitable requirement for the development of the energy internet.In reality,the wind farm's start-up planning and supply judgment must not only consider seasonal nodes and carrying loads,but also apply accurate and quantified wind power forecasting to reasonably allocate clean power supply to ensure the smooth operation of the energy network.The key to effective prediction of wind power is to deal with the complex influencing factors of wind energy and the characteristics of volatility and intermittency.In the energy Internet environment,the wind power probability prediction method that can eliminate uncertainty is more practical.In order to effectively reduce the complexity and uncertainty in wind power forecasting,continuously improve the forecasting accuracy of wind power generation power and optimize the forecasting cost.Based on the characteristics of wind energy data series,this paper proposes a probability density prediction method based on EMD and bootstrap quantile regression and a bootstrap quantile regression probability density prediction method combined with error correction model.The constructed model first uses the empirical mode decomposition(EMD)method for data preprocessing to effectively extract key data information,and secondly combines the bootstrap method,quantile regression(QR)method and error correction(EC)to propose A bootstrap quantile regression model with error correction is considered,and finally the kernel density estimation method is used to optimize the prediction performance of the model,and the experimental study of wind energy probability density prediction is completed.The results show that the model not only gives high-precision forecast values and interval ranges in the future,but also obtains the complete probability density curve of future wind power.In order to show the superiority and robustness of the proposed model method,this paper verifies from the statistical direction of the data,and evaluates the point prediction effect of the model through the mean,median and mode results provided by the probability density prediction method,while Reasonable interval prediction evaluation criteria evaluate and analyze the prediction interval.This paper mainly uses the probability density prediction method based on EMD and bootstrap quantile regression for single-step wind power forecasting,and uses the probability density prediction method based on EC and bootstrap quantile regression for multi-step wind power forecasting.In the prediction process,the original sequence characteristics,the lag matrix dimensions and the error distribution correction are considered simultaneously to build a robust and effective prediction model.The article uses four historical data sets for example analysis,including: the forecast of wind power in the winter and summer of 2019 in Ontario,Canada,and the forecast of wind power in 2018 with different frequency winter data from the Galicia wind farm in northwestern Spain.Through experimental analysis and comparison with other advanced methods,it is further proved that the wind energy probability density prediction method proposed in this paper can reduce the uncertainty and optimize the complexity while improving the prediction accuracy.In scientific research,the method in this paper provides a balanced modeling strategy for uncertainty and complexity of wind power prediction,and obtains a complete wind energy probability density curve and prediction interval,which better solves the volatility and uncertainty of wind energy The problem provides effective technical improvement and theoretical direction for the practical application of the wind power forecasting model and ensuring the stable operation of the power network.