Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression

Neurocomputing

Volumn 173, Issue , 2016, Pages 958-970

  Fan, Guo Feng ^a   Peng, Li Ling ^a   Hong, Wei Chiang ^b   Sun, Fan ^a  

^a PINGDINGSHAN UNIVERSITY   (China)

^b ORIENTAL INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Auto regression; Differential empirical mode decomposition; Electric load forecasting; Support vector regression

Indexed keywords

ELECTRIC LOAD DISPATCHING; ELECTRIC POWER PLANT LOADS; ELECTRIC UTILITIES; ENERGY TRANSFER; FORECASTING; REGRESSION ANALYSIS; SCHEDULING; SIGNAL PROCESSING;

AUTO REGRESSION; ELECTRIC LOAD DATUM; EMPIRICAL MODE DECOMPOSITION; FORECASTING PERFORMANCE; INDEPENDENT SYSTEM OPERATORS; INTRINSIC MODE FUNCTIONS; NONLINEAR LEARNING; SUPPORT VECTOR REGRESSION (SVR);

ELECTRIC LOAD FORECASTING;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUSTRALIA; AUTO REGRESSION; DIFFERENTIAL EMPIRICAL MODE DECOMPOSITION; ELECTRIC LOAD FORECASTING; FORECASTING; FUZZY SYSTEM; MATHEMATICAL COMPUTING; MEASUREMENT ACCURACY; POWER SUPPLY; PRIORITY JOURNAL; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; UNITED STATES;

EID: 84959358902     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.08.051     Document Type: Article

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