Online clustering for wind speed forecasting based on combination of RBF neural network and persistence method

This paper proposes an online clustering algorithm for wind speed forecasting. The algorithm combines the persistence method and the RBF neural network, and chooses an appropriate method according to different wind conditions. Computer simulations demonstrate that this algorithm can more accurately predict wind speed than either of the single methods and therefore is more effective for wind speed forecasting.

[1]  Tao Xue-feng.  Short-Term Wind Speed Forecasting Combing Time Series and Neural Network Method , 2008 .

[2]  Athanasios Sfetsos,et al.  A novel approach for the forecasting of mean hourly wind speed time series , 2002 .

[3]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .

[4]  S. Papathanassiou,et al.  Evaluation of two simple wind power forecasting models , 2008 .

[5]  Sancho Salcedo-Sanz,et al.  Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks , 2009, Neurocomputing.

[6]  Mohammad Monfared,et al.  A new strategy for wind speed forecasting using artificial intelligent methods , 2009 .

[7]  Xingpei Li,et al.  Wind speed prediction based on genetic neural network , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[8]  Li Yan-fei,et al.  A Wind Speed Forecasting Optimization Model for Wind Farms Based on Time Series Analysis and Kalman Filter Algorithm , 2008 .

[9]  S. Santoso,et al.  Wind power forecasting and error analysis using the autoregressive moving average modeling , 2009, 2009 IEEE Power & Energy Society General Meeting.

[10]  Liu Yongqian.  Application study of three methods in wind speed prediction , 2008 .

[11]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[12]  Bai Jie,et al.  Analysis on operation value of wind power resources , 2010, 2010 International Conference On Computer Design and Applications.

[13]  Chen Shou-sun.  Analysis on Operation Value of Wind Power Resources , 2006 .

[14]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

[15]  Andrey V. Savkin,et al.  Adaptive filtering based short-term wind power prediction with multiple observation points , 2009, 2009 IEEE International Conference on Control and Automation.