Wind speed forecasting based on Time series - Adaptive Kalman filtering algorithm

Short-term wind speed forecasting is important for the accuracy of wind power prediction. Based on convenience to establish the Time series prediction model and high accuracy of Kalman filtering algorithm, this paper proposes a hybrid algorithm to forecast wind speed combining Time series analysis and Kalman filter algorithm. First using Time series analysis theory, set up the regression forecasting model of wind speed sequence and then establish the state equation and measurement equation of Kalman filter. Because the input noise covariance and measurement noise covariance of the Kalman filtering method take the fixed value, thus we use the Adaptive Kalman filtering method, to realize hybrid forecast of the wind speed sequence. Simulation results showed that the proposed hybrid algorithm can effectively improve the predictive accuracy of wind speed, and can also solve the time delay of Time series method to predict the wind speed and lack of adaptability of Kalman filtering method exists.

[1]  Ioannis B. Theocharis,et al.  A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation , 2007, Neurocomputing.

[2]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[3]  Nabil Benoudjit,et al.  Multiple architecture system for wind speed prediction , 2011 .

[4]  Chao Chen,et al.  A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks , 2012 .

[5]  Xiaofeng Yang,et al.  A hybrid strategy of short term wind power prediction , 2013 .

[6]  Joao P. S. Catalao,et al.  Short-term wind power forecasting in Portugal by neural networks and wavelet transform , 2011 .

[7]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[8]  Chao Chen,et al.  A hybrid statistical method to predict wind speed and wind power , 2010 .

[9]  Ángel M. Pérez-Bellido,et al.  Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction , 2009 .

[10]  Ioannis B. Theocharis,et al.  Locally recurrent neural networks for wind speed prediction using spatial correlation , 2007, Inf. Sci..

[11]  Wenyu Zhang,et al.  Short-term wind speed forecasting based on a hybrid model , 2013, Appl. Soft Comput..

[12]  Mohamed Mohandes,et al.  Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS) , 2011 .

[13]  Jan Holst,et al.  Forecasting near-surface ocean winds with Kalman filter techniques , 2005 .

[14]  Heping Liu,et al.  Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed , 2011 .

[15]  Ioannis B. Theocharis,et al.  Locally recurrent neural networks for long-term wind speed and power prediction , 2006, Neurocomputing.

[16]  Jianzhou Wang,et al.  A corrected hybrid approach for wind speed prediction in Hexi Corridor of China , 2011 .

[17]  Andrew Kusiak,et al.  Very short-term wind speed forecasting with Bayesian structural break model , 2013 .

[18]  F. Cassola,et al.  Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output , 2012 .

[19]  Tomonobu Senjyu,et al.  An integrated control method for a wind farm to reduce frequency deviations in a small power system , 2011 .

[20]  Jinjin Chen.  Development of offshore wind power in China , 2011 .

[21]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[22]  Joao P. S. Catalao,et al.  A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal , 2011 .

[23]  Gabriel Garcera,et al.  Comparative study of speed estimators with highly noisy measurement signals for Wind Energy Generation Systems , 2011 .

[24]  E.F. El-Saadany,et al.  One Day Ahead Prediction of Wind Speed and Direction , 2008, IEEE Transactions on Energy Conversion.