New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks

Renewable Energy

Volumn 83, Issue , 2015, Pages 1066-1075

  Liu, Hui ^a,b   Tian, Hongqi ^a   Liang, Xifeng ^a   Li, Yanfei ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b UNIVERSITY OF ROSTOCK   (Germany)

Author keywords

Artificial Neural Networks; Decomposition; Genetic algorithm; Mind Evolutionary Algorithm; Wind energy; Wind speed forecasting

Indexed keywords

ALGORITHMS; DECOMPOSITION; ELECTRIC POWER UTILIZATION; FORECASTING; GENETIC ALGORITHMS; NEURAL NETWORKS; SPEED; WIND; WIND EFFECTS; WIND POWER;

EMPIRICAL MODEL DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; FORECASTING PERFORMANCE; GA (GENETIC ALGORITHM); MIND EVOLUTIONARY ALGORITHMS; MLP (MULTILAYER PERCEPTRON); PERFORMANCE COMPARISON; WIND SPEED FORECASTING;

EVOLUTIONARY ALGORITHMS;

ARTIFICIAL NEURAL NETWORK; EMPIRICAL ANALYSIS; ENSEMBLE FORECASTING; GENETIC ALGORITHM; PERFORMANCE ASSESSMENT; PRECISION; WIND POWER; WIND VELOCITY;

EID: 84930947539     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2015.06.004     Document Type: Article

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