An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine

Renewable Energy

Volumn 126, Issue , 2018, Pages 254-269

  Mahmoud, Tawfek ^a   Dong, Z Y ^b   Ma, Jin ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Artificial neural networks; Bootstrap sampling; Differential evolution; Self adaptive evolutionary extreme learning machine; Support vector machine; Wind power generation prediction intervals

Indexed keywords

COST FUNCTIONS; ELECTRIC POWER GENERATION; KNOWLEDGE ACQUISITION; NEURAL NETWORKS; OPTIMIZATION; QUALITY CONTROL; RISK PERCEPTION; SUPPORT VECTOR MACHINES; UNCERTAINTY ANALYSIS; WEATHER FORECASTING; WIND POWER;

BOOTSTRAP SAMPLINGS; DIFFERENTIAL EVOLUTION; DIFFERENTIAL EVOLUTION ALGORITHMS; EXTREME LEARNING MACHINE; HYBRID INTELLIGENT ALGORITHMS; SELF-ADAPTIVE DIFFERENTIAL EVOLUTIONS; STATE-OF-THE-ART TECHNOLOGY; WIND POWER GENERATION PREDICTIONS;

EVOLUTIONARY ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BOOTSTRAPPING; LEARNING; MACHINERY; POWER GENERATION; PREDICTION; SUPPORT VECTOR MACHINE; UNCERTAINTY ANALYSIS; WIND FARM; WIND POWER;

AUSTRALIA;

EID: 85046035272     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2018.03.035     Document Type: Article

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