An ensemble approach for short-term load forecasting by extreme learning machine

Applied Energy

Volumn 170, Issue , 2016, Pages 22-29

  Li, Song ^a   Goel, Lalit ^a   Wang, Peng ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Ensemble method; Extreme learning machine; Partial least squares regression; Short term load forecasting; Wavelet transform

Indexed keywords

ELECTRIC POWER PLANT LOADS; FORECASTING; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; NUMERICAL METHODS; REGRESSION ANALYSIS; WAVELET ANALYSIS; WAVELET DECOMPOSITION; WAVELET TRANSFORMS;

DECOMPOSITION LEVEL; ENSEMBLE APPROACHES; ENSEMBLE METHODS; EXTREME LEARNING MACHINE; FORECASTING MODELING; FORECASTING PERFORMANCE; PARTIAL LEAST SQUARES REGRESSION; SHORT TERM LOAD FORECASTING;

LEAST SQUARES APPROXIMATIONS;

ENSEMBLE FORECASTING; LEAST SQUARES METHOD; MACHINE LEARNING; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; WAVELET ANALYSIS;

ULMUS;

EID: 84962263030     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.02.114     Document Type: Article

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