The Study of GRNN for Wind Speed Forecasting Based on Markov Chain
Shujie Gao, Jianyan Tian, Fang Wang, Yang Bai, Wei Gao, Shengqiang Yang
Available Online August 2015.
- https://doi.org/10.2991/msam-15.2015.66How to use a DOI?
- wind speed forecasting; generalregression neural network; error correction; markov chain
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/msam-15.2015.66 ID - Gao2015/08 ER -