The Study of GRNN for Wind Speed Forecasting Based on Markov Chain

DOI

10.2991/msam-15.2015.66How to use a DOI?

Keywords

wind speed forecasting; generalregression neural network; error correction; markov chain

Abstract

In view of modeling accuracy problems of General Regression Neural Network (GRNN), the improved GRNN has been used to forecast wind speed. Firstly, K-fold cross validation was used to select smooth parameter of GRNN, and the influence of K value was analyzed. Then, Markov Chain (MC) was introduced to correct the results of GRNN to improve the accuracy of GRNN. And C-average clustering algorithm was used to state division, the number of state and correction results were emphatically discussed. Finally, the improved GRNN was applied to wind speed forecasting by use of the actual data gathered from a wind farm in Shanxi province to further test the proposed method. The experimental results show that the superiority of the proposed method compared with GRNN.

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  - Shujie Gao
AU  - Jianyan Tian
AU  - Fang Wang
AU  - Yang Bai
AU  - Wei Gao
AU  - Shengqiang Yang
PY  - 2015/08
DA  - 2015/08
TI  - The Study of GRNN for Wind Speed Forecasting Based on Markov Chain
BT  - Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics
PB  - Atlantis Press
SP  - 285
EP  - 288
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-15.2015.66
DO  - 10.2991/msam-15.2015.66
ID  - Gao2015/08
ER  -