A novel combined forecasting model for short-term wind power based on ensemble empirical mode decomposition and optimal virtual prediction

Abstract

As one of the most promising renewable energy, wind energy plays a vital role in optimizing the configuration of energy resources in power system nowadays. However, wind generation with the intermittent and uncertain characteristics has brought new challenges for the integration of large-scale wind power into power system. Consequently, the accurate forecasting of wind power is the most effective and applicable solution to meet the challenges. A novel combined forecasting approach is proposed by integrating the ensemble empirical mode decomposition (EEMD) technique and the combination of individual forecasting methods based on optimal virtual prediction for the purpose of improving the short-term wind power prediction performance. There are three steps in this presented approach. First, EEMD is adopted to decompose the original wind power series into a number of intrinsic mode functions (IMFs) and a residue. Second, the prediction of each IMF is achieved by using four individual methods, and the prediction of the residue is obtained from the nonlinear grey Bernoulli model based on particle swarm optimization. Finally, the combined forecasting model based on optimal virtual prediction is developed, and the weight matrix in this model is optimized by a self-adaptive differential evolution algorithm, which aims to minimize the forecasting errors at the virtual prediction points. The real wind power data from a wind farm in China are used to verify the performance of the proposed model, and the simulation results show that this model has demonstrated the optimal forecasting accuracy and robustness compared with other forecasting models, which is a promising alternative for short-term wind power forecasting.