A self-adaptive hybrid approach for wind speed forecasting

Wind power, as a promising renewable energy source, has environmental benefits, as well as economic and social ones. To evaluate wind energy properly and efficiently, this study proposes a hybrid forecasting approach that combines the Extreme Learning Machine (ELM), which rarely presents in literature on wind speed forecasting, the Ljung-Box Q-test (LBQ) and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) to enhance the accuracy of wind speed forecasting. The proposed hybrid method is examined by forecasting the mean daily and mean monthly wind speed of four wind speed observation sites located in northwestern China. The results confirm that, compared with other popular models (ARIMA，SARIMA，Back-Propagation neural network (BP) and ELM), the hybrid forecasting method improves the predictions of daily and monthly wind speed, which indicates that the developed hybrid method exhibits stronger forecasting ability. The forecasting results also suggest the hybrid approach has better generality and practicability in different wind farms.

[1]  Ali Azadeh,et al.  A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran , 2008 .

[2]  Qunzhan Li,et al.  Short-term wind speed syntheses correcting forecasting model and its application , 2013 .

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

[4]  Gong Li,et al.  Application of Bayesian model averaging in modeling long-term wind speed distributions , 2010 .

[5]  Ruey S. Tsay,et al.  Analysis of Financial Time Series , 2005 .

[6]  Haiyan Lu,et al.  A case study on a hybrid wind speed forecasting method using BP neural network , 2011, Knowl. Based Syst..

[7]  Jianzhou Wang,et al.  Forecasting wind speed using empirical mode decomposition and Elman neural network , 2014, Appl. Soft Comput..

[8]  Jianwu Dang,et al.  Fuzzy rough regression with application to wind speed prediction , 2014, Inf. Sci..

[9]  John B. Guerard,et al.  Comparing the effectiveness of traditional vs. mechanized identification methods in post-sample forecasting for a macroeconomic Granger causality analysis , 2015 .

[10]  Hui Liu,et al.  Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction , 2012 .

[11]  Jianzhou Wang,et al.  A hybrid forecasting approach applied to wind speed time series , 2013 .

[12]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[13]  Jing Shi,et al.  Bayesian adaptive combination of short-term wind speed forecasts from neural network models , 2011 .

[14]  Hui Liu,et al.  Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions , 2015 .

[15]  Farshid Keynia,et al.  Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network , 2010 .

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

[17]  Haiyan Lu,et al.  Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model , 2012 .

[18]  Tsang-Jung Chang,et al.  Estimation of monthly wind power outputs of WECS with limited record period using artificial neural networks , 2012 .

[19]  Maria Grazia De Giorgi,et al.  Assessment of the benefits of numerical weather predictions in wind power forecasting based on stati , 2011 .

[20]  Qing Cao,et al.  Forecasting wind speed with recurrent neural networks , 2012, Eur. J. Oper. Res..

[21]  Rongning Wu.  Least absolute deviation estimation for general fractionally integrated autoregressive moving average time series models , 2014 .

[22]  Julian Meng,et al.  Short-Term Wind Speed Forecasting Based On Fuzzy Artmap , 2011 .

[23]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Jing Shi,et al.  Evaluation of hybrid forecasting approaches for wind speed and power generation time series , 2012 .

[25]  Ali Azadeh,et al.  An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran , 2010 .

[26]  K. Gnana Sheela,et al.  Neural network based hybrid computing model for wind speed prediction , 2013, Neurocomputing.

[27]  Dongxiao Niu,et al.  Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm , 2014 .

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

[29]  Andrew F. Siegel,et al.  Practical Business Statistics , 1994 .

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

[31]  Paul Leahy,et al.  Comparison of linear, Kalman filter and neural network downscaling of wind speeds from numerical weather prediction , 2011, 2011 10th International Conference on Environment and Electrical Engineering.

[32]  D. Fadare.  The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria , 2010 .

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

[34]  Taehoon Hong,et al.  An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network) , 2014 .

[35]  Feng Gao,et al.  Sparse online warped Gaussian process for wind power probabilistic forecasting , 2013 .

[36]  Erasmo Cadenas,et al.  Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model , 2010 .

[37]  Ismael Sánchez,et al.  Adaptive combination of forecasts with application to wind energy , 2008 .

[38]  Jing Shi,et al.  Fine tuning support vector machines for short-term wind speed forecasting , 2011 .