Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

Applied Energy

Volumn 113, Issue , 2014, Pages 690-705

  Chen, Kuilin ^a   Yu, Jie ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Dynamic uncertainty; Renewable wind energy; Stochastic system; Support vector regression; Unscented Kalman filter; Wind speed prediction

Indexed keywords

COMPUTER SIMULATION; ELECTRIC POWER GENERATION; ELECTRIC UTILITIES; FORECASTING; MODEL PREDICTIVE CONTROL; NEURAL NETWORKS; NONLINEAR FILTERING; OPTIMIZATION; PREDICTIVE CONTROL SYSTEMS; REGRESSION ANALYSIS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; VECTORS; WIND EFFECTS; WIND POWER;

DYNAMIC UNCERTAINTY; RENEWABLE WIND ENERGY; SUPPORT VECTOR REGRESSION (SVR); UNSCENTED KALMAN FILTER; WIND SPEED PREDICTION;

VECTOR SPACES;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CONTROL SYSTEM; KALMAN FILTER; POWER GENERATION; PREDICTION; REGRESSION ANALYSIS; RENEWABLE RESOURCE; STOCHASTICITY; UNCERTAINTY ANALYSIS; WIND FARM; WIND POWER; WIND TURBINE; WIND VELOCITY;

MASSACHUSETTS; UNITED STATES;

EID: 84883355288     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2013.08.025     Document Type: Article

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