A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system

Renewable Energy

Volumn 103, Issue , 2017, Pages 620-629

  Liu, Jinqiang ^a   Wang, Xiaoru ^a   Lu, Yun ^a  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

Author keywords

Adaptive neuro fuzzy inference system; Hybrid methodology; Least squares support vector machine; Neural network; Pearson correlation coefficient; Short term wind power forecasting

Indexed keywords

ASSOCIATION RULES; CORRELATION METHODS; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES; TWO PHASE FLOW; WEATHER FORECASTING; WIND POWER;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; HYBRID METHODOLOGIES; LEAST SQUARES SUPPORT VECTOR MACHINES; PEARSON CORRELATION COEFFICIENTS; SHORT-TERM WIND POWER FORECASTING;

FUZZY INFERENCE;

ARTIFICIAL NEURAL NETWORK; DATA PROCESSING; FORECASTING METHOD; LEAST SQUARES METHOD; POWER GENERATION; SMART GRID; SUPPORT VECTOR MACHINE; WIND POWER;

CHINA;

EID: 85006355806     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2016.10.074     Document Type: Article

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