Wind Speed Forecasting Modelling by Combination of Masking Signal Based Empirical Mode Decomposition and GA-BP Neural Network

doi:10.11930/j.issn.1004-9649.2014.5.129.6

Abstract

Abstract: For wind power forecasting, the traditional empirical mode decomposition method usually decompose the wind speed signal into several components with different frequencies. However, the mode-mixing phenomenon may exist and affect the accuracy of forecasting. To solve the problem, a new combination model based on the advanced empirical mode decomposition(EMD) and GA-BP neural network was proposed. Firstly, the traditional empirical mode is improved by the masking signal method(MS), by which the original data can be decomposed into more stationary signals with different frequencies. Secondly, each signal is taken as the input data to establish GA-BP neural network forecasting model. Finally, the forecasting results can be obtained by adding up the predicted data of each signal. The proposed method was programmed by C++ and tested by using the data from an actual wind farm. The simulation experiments show that the proposed method can improve the forecasting accuracy and its running time is also short, which is suitable for ultra short term(10 min) and short term (1hour) wind speed forecasting on line. Consequently, it has a certain practical significance.

Key words: improved empirical mode decomposition, wind speed forecasting, masking signal (MS), short term forecasting, ultra short term forecasting

CLC Number:

TM614

ZHANG Na, WANG Shou-xiang, WANG Ya-min. Wind Speed Forecasting Modelling by Combination of Masking Signal Based Empirical Mode Decomposition and GA-BP Neural Network[J]. Electric Power, 2014, 47(5): 129-135.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2014.5.129.6

https://www.electricpower.com.cn/EN/Y2014/V47/I5/129

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