Ultra-Short-Term wind speed prediction using RBF Neural Network

DOI

10.2991/isci-15.2015.317How to use a DOI?

Keywords

wind speed; radial basis function (RBF); ultra-short-time prediction

Abstract

As a renewable and clean energy source, wind power is being widely utilized all over the world. The uncertainty of wind speed makes certain trouble for the development of wind power generation. In order to relieve the disadvantageous impact of wind speed intermittence on connected power system, this paper proposes a radial basis function (RBF) neural network-based prediction model for ultra-short-term wind speed. Simulation studies are carried out to validate the proposed model for ultra-short-term wind speed by using data obtained from a wind farm from Beijing. The performance of the RBF neural network is compared with that of BP network. Results show that the RBF prediction model significantly outperforms the BP model.

Copyright

© 2015, 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  - Gao-cheng Cao
AU  - Dao-huo Huang
PY  - 2015/01
DA  - 2015/01
TI  - Ultra-Short-Term wind speed prediction using RBF Neural Network
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 2441
EP  - 2448
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.317
DO  - 10.2991/isci-15.2015.317
ID  - Cao2015/01
ER  -