Wind speed forecasting method based on eemd and quantum bacterial foraging optimization

© 2015, Science Press. All right reserved. In view of the intrinsic characteristics of wind speed sequence, a wind speed forecasting method based on empirical mode ensemble empirical mode decomposition (EEMD) was proposed to improve the mode mixing problem of empirical mode decomposition (EMD). To solve the problem of the uncertain parameters in wind speed forecasting method, a quantum bacterial foraging optimization (QBFO) algorithm was proposed by introducing quantum behavior to the reproduction operator. And then the probability distribution model in quantum space was used to optimize the selection of parameters. The parameter optimizing results of four algorithms show that QBFO has better global search accuracy and generalization performance than other algorithms. By applying the QBFO algorithm to optimize combined forecasting methods, the case study shows the superiority of EEMD in solving the mode mixing problem and the higher accuracy of wind power prediction than EMD model.