Abstract
The output of the wind turbine has high randomness due to natural wind velocity. Whether the output can be predicted accurately or not is directly related to the feasibility of dispatching wind power in the power network. The key of wind farm output prediction is to predict the wind speed of wind farm site. This paper uses AR model and BP neural network to predict 24-hour wind speed, and proves the feasibility of these two predicted methods according to comparison with measured wind speed data. This paper has certain reference significance for improving the precision of wind speed prediction.
Publisher
Trans Tech Publications, Ltd.
Reference8 articles.
1. Hai-yang Luo, Tian-qi Liu, Xing-yuan Li. Chaotic Forecasting Method of Short-Term Wind Speed in Wind Farm. Power System Technology, 2009, 33(9): p.67~71.
2. Tai-hua Chang, Lu Wang, Wei Ma. Wind Speed Prediction Based on AR, ARIMA Model. East China Electric Power, 2010, 38(1): p.59~62.
3. Christophe Sibuet Watters, Paul Leahy. Comparison of linear, Kalman filter and neural network downscaling of wind speeds from numerical weather prediction. 2011 10th International Conference on Environment and Electrical Engineering (EEEIC): p.1.
4. Wen-sheng Wang, Jing Ding, Ju-liang Jin. Stochastic hydrology (The second edition) . Beijing: China Waterpower Press, (2008).
5. S.A. Pourmousavi Kani, M.M. Ardehali. Very short-term wind speed prediction: A new artificial neural network–Markov chain model. Energy Conversion and Management, 2011, 52(1), p.738~740.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献