Title :
Ultra-short-term wind power prediction using BP neural network
Author :
Jinxuan Li ; Jiandong Mao
Author_Institution :
Sch. of Electr. & Inf. Eng., Beifang Univ. of Nat., Yinchuan, China
Abstract :
As a newly renewable energy, wind power is one of the fastest growing and the most mature power generation technology. Based on the historical numerical weather prediction and corresponding wind power output data, an ultra short-term wind power prediction method for future four hours is presented and a prediction model based on BP neural network is established. Some experiments are performed. The results show that the prediction method is feasible and has important reference value for similar wind power prediction system.
Keywords :
load forecasting; neural nets; power generation planning; renewable energy sources; weather forecasting; wind power plants; BP neural network; historical numerical weather prediction; power generation technology; renewable energy; ultrashort-term wind power prediction method; Biological neural networks; Predictive models; Wind forecasting; Wind power generation; Wind speed; BP Neural Network; Power System; Ultra-Short-Term; Wind Power Prediction;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4316-6
DOI :
10.1109/ICIEA.2014.6931497