Wind power short-term prediction based on SVM trained by improved FOA
- https://doi.org/10.2991/icaees-15.2015.11How to use a DOI?
- wind power prediction; prediction accuracy; support vector machine; optimizing; assessment
The forecast accuracy of the wind power directly affects the operating cost of the network system, which is directly related to the supply and demand balance grid. Therefore, the forecast accuracy of wind power is very important. Considering the prediction accuracy not high, we propose an improved predictive method that is based on FOA-SVM. Since SVM penalty factor and kernel parameters having a great impact on the forecast Intensive, thus the improved FOA optimizes the parameters of support vector machine and train model with a good parameter optimization .Then the built model is used to the power prediction and evaluates the data finally. The prediction results show: the improved FOA-SVM can produce wind power prediction accuracy better.
- © 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 - Feng Xiao AU - Guochu Chen PY - 2015/07 DA - 2015/07 TI - Wind power short-term prediction based on SVM trained by improved FOA BT - Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science 2015 PB - Atlantis Press SP - 54 EP - 61 SN - 2352-5401 UR - https://doi.org/10.2991/icaees-15.2015.11 DO - https://doi.org/10.2991/icaees-15.2015.11 ID - Xiao2015/07 ER -