Study on Combination Model of Wind Power Generation Prediction
Authors
Guan-qi LIU, Ting HU, Long Shao
Corresponding Author
Guan-qi LIU
Available Online March 2013.
- DOI
- 10.2991/iccsee.2013.136How to use a DOI?
- Keywords
- wind power prediction, neural work, time series, support vector machine, combined forecasting
- Abstract
With the installed wind capacity increasing rapidly, the security, stability and economic operation of the power grid have been influenced because of the randomness and volatility of the wind power. Wind power prediction is an effective approach for the above problems. This article is about the theory of combination forecast and establishes two combination forecast models by combining RBF network power prediction model, time series model and support vector machine (SVM) model. Finally, through comparative analysis of the results, combination model can get better prediction accuracy, and better meets the actual needs.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Guan-qi LIU AU - Ting HU AU - Long Shao PY - 2013/03 DA - 2013/03 TI - Study on Combination Model of Wind Power Generation Prediction BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) PB - Atlantis Press SP - 531 EP - 534 SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.136 DO - 10.2991/iccsee.2013.136 ID - LIU2013/03 ER -