Title :
Wind Speed Forecasting Based on Combination Forecasting Model
Author :
Xiaoqiang, Nan ; Qunzhan, Li ; Junxiang, Yu ; Zhiyu, You
Author_Institution :
Sch. of Electr. Eng., Southwest Jiaotong Univ., Chengdu, China
Abstract :
The accuracy of wind speed forecasting is related to the wind power scheduling. When large-scale wind power connected grid, it also affects the stability of the grid. This paper applies time series model and Back Propagation (BP) neural network model to predict wind speed. Finally, a combination model of time series and BP neural network is proposed. In the combination model, the inputs of BP neural network are made up of historical data and residual errors calculated by time series model. The model can be more accurately in the short-time wind speed forecasting. And then shows an actual example.
Keywords :
backpropagation; neural nets; power engineering computing; power grids; scheduling; time series; wind power; backpropagation; grid stability; large scale wind power connected grid; neural network model; short time wind speed forecasting; time series combination model; wind power scheduling; Artificial neural networks; Data models; Forecasting; Mathematical model; Predictive models; Time series analysis; Wind speed; BP neural network model; combination model; time series model; wind speed forecasting;
Conference_Titel :
Information Science and Management Engineering (ISME), 2010 International Conference of
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-7669-5
Electronic_ISBN :
978-1-4244-7670-1
DOI :
10.1109/ISME.2010.45
