Short-term Wind Power Prediction Based on Particle Filter and Radial Basis Function Neural Network
- 10.2991/asei-15.2015.40How to use a DOI?
- Particle Filter; RBF; Prediction Model; Wind Power; Renewable Energy
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.
- © 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/).
Cite this article
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 -