Takagi-Sugeno fuzzy model-based approach considering multiple weather factors for the photovoltaic power short-term forecasting

IET Renewable Power Generation

Volumn 11, Issue 10, 2017, Pages 1281-1287

  Liu, Fang ^a   Li, Ranran ^a   Li, Yong ^b   Yan, Ruifeng ^c   Saha, Tapan ^c  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b HUNAN UNIVERSITY   (China)

^c UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; CLUSTERING ALGORITHMS; ELECTRIC POWER GENERATION; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; PHOTOVOLTAIC CELLS; RECURRENT NEURAL NETWORKS; SUPPORT VECTOR MACHINES; WIND;

BACK PROPAGATION NEURAL NETWORKS; EMPIRICAL MODE DECOMPOSITION; FORECASTING PERFORMANCE; FUZZY C-MEAN CLUSTERING ALGORITHM; PHOTOVOLTAIC SYSTEMS; RECURSIVE LEAST SQUARES METHOD; SHORT-TERM FORECASTING; TAKAGI SUGENO FUZZY MODELS;

FORECASTING;

EID: 85029805316     PISSN: 17521416     EISSN: 17521424     Source Type: Journal    
DOI: 10.1049/iet-rpg.2016.1036     Document Type: Article

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