An improved neural network-based approach for short-term wind speed and power forecast

Renewable Energy

Volumn 105, Issue , 2017, Pages 301-311

  Chang, G W ^a   Lu, H J ^a   Chang, Y R ^b   Lee, Y D ^b  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^b INSTITUTE OF NUCLEAR ENERGY RESEARCH   (Taiwan)

Author keywords

Forecast; Radial basis function neural network; Wind power; Wind speed

Indexed keywords

COMPUTATIONAL EFFICIENCY; ELECTRIC POWER GENERATION; ELECTRIC POWER SYSTEM INTERCONNECTION; ELECTRIC UTILITIES; FORECASTING; FUNCTIONS; RADIAL BASIS FUNCTION NETWORKS; SPEED; WIND EFFECTS; WIND POWER;

FORECASTING METHODS; GAUSSIAN BASIS FUNCTIONS; INITIALIZATION METHODS; LOOK-AHEAD TIME; NETWORK-BASED APPROACH; NEURAL NETWORK TRAINING; RADIAL BASIS FUNCTION NEURAL NETWORKS; WIND SPEED;

WEATHER FORECASTING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; ERROR ANALYSIS; FORECASTING METHOD; NETWORK ANALYSIS; POWER GENERATION; WIND POWER; WIND VELOCITY;

TAIWAN;

EID: 85007494524     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2016.12.071     Document Type: Article

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