Short Term Wind Power Forecasting Using Autoregressive Integrated Moving Average Approach
Short Term Wind Power Forecasting Using Autoregressive Integrated Moving Average Approach
摘要
Wind energy is one of the most promising electricity generating sources as a clean and free alternate compared with the conventional power plants and due to the volatility feature in the wind speeds it will reflect some problems in power systems reliability particularly if the system is deeply penetrated by wind farms. Therefore, wind power forecasting issue become and is still an important scope that will help in ED （economic dispatch）, UC （unit commitment） purposes to get more reliable and economic systems. This paper introduces short term wind power forecasting model, based on ARIMA （autoregressive integrated moving average） which will be applied to hourly wind data from Zaafarana 5 project in Egypt. The proposed model successfully outperforms the persistence model with significant improvement up to 6 h ahead.
参考文献24
-
1L. Landberg, Short-term prediction of the power production from wind farms, Journal of Wind Engineering and Industrial Aerodynamics 80 (112) (2009) 207-220.
-
2M. Bhaskar, A. Jain, N.V. Srinath, Wind speed forecasting: Present status, in: 2010 International Conference on Power System Technology, Hangzhou, 2010.
-
3G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, C. Draxl, The State of the Art in Short-Term Prediction of Wind Power, A Literature Overview, 2nd ed., Jan. 2011.
-
4C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang, G. Conzelrnann, Wind Power Forecasting: State-of-the-Art, Argonne National Laboratory ANLIDIS-lO-l, Nov. 2009.
-
5L. Landberg, Short-term prediction of local wind conditions, Ph.D. Dissertation, Rise National Laboratory, Roskilde, Denmark, 1994.
-
6U. Focken, M. Lange, H.P. Waldl, Previento-A wind power prediction system with an innovative upscaling algorithm, in: Proceedings of the European Wind Energy Conference EWEC'OI, Copenhagen, Denmark, June 2-6, 2001, pp. 826-829.
-
7I. Marti, D. Cabez6n, 1. Villanueva, M.J. San Isidro, Y. Loureiro, E. Cantero, et al., LocalPred and RegioPred, advanced tools for wind energy prediction in complex terrain, in: Proceedings of the European Wind Energy Conference EWEC'03, Madrid, Spain, June 16-19,2003.
-
8M.A. Kulkarni, S. Patil, G.V. Rama, P.N. Sen, Wind speed prediction using statistical regression and neural network, Journal of Earth System Science 117 (4) (2008) 457-463.
-
9T.H.M. El-Fouly, E.F. El-Saadany, M.M.A. Salama, Improved grey predictor rolling models for wind power prediction, IEEE Trans. Power Syst. 21 (3) (2006) 1450-1452.
-
10R. Li, Y. Wang, Short-term wind speed forecasting for wind farm based on empirical mode decomposition, in: Proceedings of Electrical Machines and Systems, ICEMS 2008, Wuhan, Oct. 17-20,2008.
-
1赵文.用移动平均法预测农村用电量[J].农村电气化,1994(12):17-17.
-
2王君.变电站自动化技术分析[J].中国新技术新产品,2012(22):172-173.
-
3In-Yong Seo,Bok-Nam Hat,Sang-Ok Kin,Won Nam-Koong,Dong-Wan Seo,Seong-JunKim.Short Term Wind Power Prediction Using Wavelet Transform and ARIMA[J].Journal of Energy and Power Engineering,2012,6(11):1786-1790.
-
4孟婷婷,何利力.移动平均法在卷烟产量预测中的应用[J].工业控制计算机,2015,28(5):126-126. 被引量：3
-
5陈峥杉.时间序列预测的研究与实现[J].福建电脑,2004,20(11):9-11. 被引量：8
-
6邵珊珊,孙丽君.基于Elman神经网络的燃气轮机功率预测方法研究[J].计算机科学与探索,2014,8(11):1358-1364. 被引量：6
-
7付超,李奎.基于支持向量机的高压断路器故障诊断方法的研究[J].河北工业大学学报,2015,44(4):12-17. 被引量：1
-
8薛冰,刘准.风电功率Web服务平台的设计[J].电子测试,2013,24(11):92-94.
-
9刘晓杰,何迪.一种基于功率控制的无线传感器网络MAC协议[J].信息技术,2008,32(7):9-12. 被引量：1
-
10林林,权先璋,向廷元.计算机交流采样的算法与实现[J].计量与测试技术,1995,22(3):21-22.
