Short-term wind power prediction based on EEMD–LASSO–QRNN model

Abstract

With the increasing utilization of wind generation in power system, the improvement of wind power forecasting precision is attached vital importance. Owing to the stochastic and intermittent nature of wind power, the conventional methods no longer ensure sufficient accuracy of wind power prediction in majority of scenarios. Motivated by recent advancements of ensemble methods based on decomposition technologies, a novel ensemble method based on ensemble empirical mode decomposition (EEMD) and least absolute shrinkage and selection operator–quantile regression neural network (LASSO–QRNN) model for forecasting wind power is proposed in this paper. The model is an ingenious integration of data preprocessing technology, feature selection method, prediction model and data post-processing technology. Thereinto, EEMD is exploited to convert intricate and irregular wind power time series into a collection of subseries relatively easy to analyze; LASSO regression is combined with QRNN model to realize the filtering of important variables and provide more comprehensive and robust prediction results; the KDE method reprocesses the prediction results, greatly improves the prediction accuracy and effectively quantifies the uncertainty of the forecasting process. The suggested model and several benchmark models have been implemented on six wind power datasets, two are gathered from a wind farm in Spain and four from a competition to demonstrate the superiorities of the model proposed in this paper. The compared results reveal that the proposed method has adequate capacity to enhance the performance of wind power forecasting, measure and reduce the uncertainty of prediction process.