Ultra-short-term Wind Power Forecasting Based on CEEMD and Chaos Theory

doi:10.16182/j.issn1004731x.joss.201804043

Abstract

Abstract: This paper studies the ultra-short-term prediction of wind power generating capacity by means of CEEMD and chaos theory. Wind power time series is decomposed by CEEMD to decrease the non-stationary of time series. CEEMD can overcome the modal aliasing problem of EMD. The phase space reconstruction method is used to extract characteristics of each sequence, which provides the basis for the selection of input dimension when building a model. The least squares support vector machine models are built for each sequence and the prediction are made separately. The predicted results are added to get the final prediction. Simulation is performed to the real data from a wind farm of Inner Mongolia. The results show that the proposed method is effective and the mean absolute error decreases by 3.8% compared with the conventional EMD and neural network model.

Key words: wind power, forecasting, CEEMD, least squares support vector machine, phase space reconstruction

CLC Number:

TP391.9

Wang Lijie, Zhang Li, Zhang Yan. Ultra-short-term Wind Power Forecasting Based on CEEMD and Chaos Theory[J]. Journal of System Simulation, 2018, 30(4): 1560-1565.

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