A self-adaptive hybrid approach for wind speed forecasting

Renewable Energy

Volumn 78, Issue , 2015, Pages 374-385

  Wang, Jianzhou ^a   Hu, Jianming ^a   Ma, Kailiang ^b   Zhang, Yixin ^a  

^a LANZHOU UNIVERSITY   (China)

^b Petrochina Planning and Engineer Institute   (China)

Author keywords

Extreme learning machine; Hybrid forecasting; Wind speed

Indexed keywords

BACKPROPAGATION; ELECTRIC UTILITIES; FORECASTING; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; NEURAL NETWORKS; RENEWABLE ENERGY RESOURCES; SPEED; WIND EFFECTS; WIND POWER;

AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; BACK PROPAGATION NEURAL NETWORKS; ENVIRONMENTAL BENEFITS; EXTREME LEARNING MACHINE; HYBRID FORECASTING; RENEWABLE ENERGY SOURCE; WIND SPEED; WIND SPEED FORECASTING;

WIND;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; FORECASTING METHOD; RENEWABLE RESOURCE; WIND FARM; WIND POWER; WIND VELOCITY;

CHINA;

EID: 84921916753     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2014.12.074     Document Type: Article

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