Short-term Wind Power Prediction Based on Particle Filter and Radial Basis Function Neural Network

DOI

10.2991/asei-15.2015.40How to use a DOI?

Keywords

Particle Filter; RBF; Prediction Model; Wind Power; Renewable Energy

Abstract

In order to improve the accuracy of wind power prediction, this paper proposed a new short-term wind power prediction combined method based on particle filter and radial basis function neural network. First, the historical wind speed data are processed with particle filter, and the processed wind speed data combined the historical wind direction data and temperature data are using as the input data of the forecast model. Then, the PF-RBF neural network of wind power output forecasting model is established according to the new input data. The experimental results show that the proposed forecasting model has a good accuracy for wind power prediction.

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  - Yongxiang Wang
AU  - Guochu Chen
PY  - 2015/05
DA  - 2015/05
TI  - Short-term Wind Power Prediction Based on Particle Filter and Radial Basis Function Neural Network
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 188
EP  - 194
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.40
DO  - 10.2991/asei-15.2015.40
ID  - Wang2015/05
ER  -