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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
