Study on Combination Model of Wind Power Generation Prediction

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/).

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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  -