Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Renewable Energy

Volumn 94, Issue , 2016, Pages 629-636

  Wang, Shouxiang ^a   Zhang, Na ^b   Wu, Lei ^c   Wang, Yamin ^c  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b Hulunbuir University   (China)

^c Clarkson University   (United States)

Author keywords

BP neural network; EEMD; EMD; GA; Wind speed forecasting

Indexed keywords

COMPUTATIONAL EFFICIENCY; FORECASTING; GALLIUM; MATLAB; NEURAL NETWORKS; SENSITIVITY ANALYSIS; SIGNAL PROCESSING; SPEED; STOCHASTIC SYSTEMS; WIND EFFECTS; WIND POWER;

BP NEURAL NETWORKS; EEMD; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITIONS (EEMD); IMPROVED EMPIRICAL MODE DECOMPOSITIONS (EMD); WAVELET NEURAL NETWORKS; WIND POWER PREDICTIONS; WIND SPEED FORECASTING;

WIND;

ARTIFICIAL NEURAL NETWORK; DECOMPOSITION ANALYSIS; FORECASTING METHOD; SENSITIVITY ANALYSIS; SOFTWARE; WAVELET ANALYSIS; WIND FARM; WIND POWER; WIND VELOCITY;

CHINA; NEI MONGGOL;

EID: 84962148959     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2016.03.103     Document Type: Article

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