A hybrid wind speed forecasting strategy based on Hilbert-Huang transform and machine learning algorithms

POWERCON 2014 - 2014 International Conference on Power System Technology: Towards Green, Efficient and Smart Power System, Proceedings

Volumn , Issue , 2014, Pages 2980-2986

  Tomin, Nikita ^a   Sidorov, Denis ^a   Kurbatsky, Victor ^a   Spiryaev, Vadim ^a   Zhukov, Alexey ^b   Leahy, Paul ^c  

^a MELENTIEV ENERGY SYSTEMS INSTITUTE   (Russian Federation)

^b IRKUTSK STATE UNIVERSITY   (Russian Federation)

^c UNIVERSITY COLLEGE CORK   (Ireland)

Author keywords

forecasting; Hilbert Huang transform; machine learning; power systems; wind power

Indexed keywords

DECISION TREES; FORECASTING; FORESTRY; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; OFFSHORE OIL WELL PRODUCTION; STANDBY POWER SYSTEMS; SUPPORT VECTOR MACHINES; SUPPORT VECTOR REGRESSION; TIME SERIES ANALYSIS; WIND; WIND POWER;

ADAPTIVE APPROACH; EVALUATION RESULTS; HILBERT HUANG TRANSFORMS; MACHINE LEARNING MODELS; NON-STATIONARY TIME SERIES; REGRESSION TREES; WIND RESOURCE ASSESSMENT; WIND SPEED FORECASTING;

LEARNING ALGORITHMS;

EVALUATION; FORECASTS; MODELS; POWER; REGRESSION ANALYSIS;

EID: 84924364709     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/POWERCON.2014.6993990     Document Type: Conference Paper

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