A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

[1]  W. Rivera,et al.  Wind speed forecasting in the South Coast of Oaxaca, México , 2007 .

[2]  Pierre Pinson,et al.  On‐line assessment of prediction risk for wind power production forecasts , 2003 .

[3]  Hongjie Wang,et al.  ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting , 2009, Artificial Intelligence and Computational Intelligence.

[4]  Peter York,et al.  Optimisation of the predictive ability of artificial neural network (ANN) models: a comparison of three ANN programs and four classes of training algorithm. , 2005, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[5]  Tao Li.  A Survey on Wavelet Applications in Data Mining , 2009 .

[6]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

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

[8]  Ted Jaditz,et al.  MULTIVARIATE NONLINEAR FORECASTING Using Financial Information to Forecast the Real Sector , 1998 .

[9]  R.W. Dunn,et al.  One-hour-ahead wind speed prediction using a Bayesian methodology , 2006, 2006 IEEE Power Engineering Society General Meeting.

[10]  Andrew K. Chan,et al.  Fundamentals of Wavelets: Theory, Algorithms, and Applications , 2011 .

[11]  Eugene Fernandez,et al.  Analysis of wind power generation and prediction using ANN: A case study , 2008 .

[12]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

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

[14]  Yudong Zhang,et al.  Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network , 2009, Expert Syst. Appl..

[15]  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.

[16]  A. Tapia,et al.  Application of a control algorithm for wind speed prediction and active power generation , 2005 .

[17]  Martin Vetterli,et al.  Spatially adaptive wavelet thresholding with context modeling for image denoising , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[18]  Pietro Vecchio,et al.  Wind energy prediction using a two-hidden layer neural network , 2010 .

[19]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[20]  I. Daubechies,et al.  Wavelets on the Interval and Fast Wavelet Transforms , 1993 .

[21]  Wenyu Zhang,et al.  A novel hybrid approach for wind speed prediction , 2014, Inf. Sci..

[22]  Mehrdad Abedi,et al.  Short term wind speed forecasting for wind turbine applications using linear prediction method , 2008 .

[23]  Linda See,et al.  Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning , 2006 .

[24]  G. H. Riahy,et al.  Very short-term wind speed prediction using linear regression among ANN and Markov chain , 2008 .

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

[26]  W. Rivera,et al.  Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks , 2009 .

[27]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[28]  Seyed Hossein Hosseinian,et al.  A new integrated approach for very short-term wind speed prediction using wavelet networks and PSO , 2007 .

[29]  Sanjay V. Dudul,et al.  Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network , 2005, Appl. Soft Comput..

[30]  L. Kamal,et al.  Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan , 1997 .

[31]  P. McNelis.  Neural networks in finance : gaining predictive edge in the market , 2005 .

[32]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[33]  James B. Ramsey,et al.  Wavelets in Economics and Finance: Past and Future , 2002 .

[34]  Jaideva C. Goswami,et al.  Fundamentals of wavelets , 1999 .

[35]  Mary E. Thomson,et al.  Performance evaluation of judgemental directional exchange rate predictions B , 2005 .

[36]  I. Johnstone,et al.  Minimax estimation via wavelet shrinkage , 1998 .

[37]  Chi-Jie Lu,et al.  Integrating independent component analysis-based denoising scheme with neural network for stock price prediction , 2010, Expert Syst. Appl..

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

[39]  Lazar Lazić,et al.  Wind forecasts for wind power generation using the Eta model , 2010 .

[40]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[41]  Nabil Kallas,et al.  Modeling soil collapse by artificial neural networks , 2004 .

[42]  A. Shamshad,et al.  First and second order Markov chain models for synthetic generation of wind speed time series , 2005 .

[43]  Mamun Bin Ibne Reaz,et al.  Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction , 2009, Expert Syst. J. Knowl. Eng..

[44]  Shenghuo Zhu,et al.  A survey on wavelet applications in data mining , 2002, SKDD.

[45]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[46]  X. Wang,et al.  Wind speed forecasting for power system operational planning , 2005, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

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

[48]  S.A.P. Kani,et al.  A new ANN-based methodology for very short-term wind speed prediction using Markov chain approach , 2008, 2008 IEEE Canada Electric Power Conference.

[49]  E.F. El-Saadany,et al.  One day ahead prediction of wind speed using annual trends , 2006, 2006 IEEE Power Engineering Society General Meeting.

[50]  Z. Şen,et al.  First-order Markov chain approach to wind speed modelling , 2001 .

[51]  Heping Liu,et al.  Prediction of wind speed time series using modified Taylor Kriging method , 2010 .

[52]  Brandon Whitcher,et al.  Differentiating intraday seasonalities through wavelet multi-scaling , 2001 .

[53]  H. Kantz,et al.  Markov chain model for turbulent wind speed data , 2004 .